Chapter 1. Introduction

Chapter 1. Introduction The Exponential Growth of Biomedical Research Data The current capabilities of our biomedical research enterprise, exemplified by the completion of Human Genome Project, enable researchers to quickly and routinely survey the contents of entire molecular and cellular systems. This capability is generating a revolution in biomedical research in various profound ways. One significant change is the availability of staggering amounts of genomic and functional genomic data gathered at a whole genome or whole cell scale. As the result of such tremendous technology breakthroughs, the challenge for biomedical research is being shifted from experimental data generation to the organization, curation and interpretation of these data (Lander ES et al, 2001; Meldrum D et al, 2000). Biomedical research literature can be considered to be a knowledgebase that comprises the most complete status of our research enterprise. Reflecting the geometric growth of available experimental data, the publication rate in biomedicine is also increasing exponentially. There are currently more than 17 million biomedical articles already represented in the National Library of Medicine’s biomedical literature database MEDLINE, including more than 3 million articles published within last 5 years alone and 2,000 per day in 2006 (Hunter L et al, 2006; MEDLINE). Keeping abreast of this large and ever-expanding body of information is increasingly daunting for researchers in order to track and utilize what’s relevant to their interests, especially for new investigators. For example, the pediatric tumor neuroblastoma is a common pediatric tumor but considered to be quite rare overall, with approximately 600 new cases diagnosed in the US each 1

year. However, there are almost 25,000 research articles describing neuroblastoma, making it virtually impossible for a new investigator to systematically assess historical research on this topic. Furthermore, researchers have the increasing need to get in touch with the research fields outside their core competence. The commonly used PubMed system, which provides a convenient query interface for MEDLINE, provides keyword search and some concept mapping for researchers to narrow down the information they are looking for (PubMed). However, its capabilities lack the precision (positive predictive value), recall (sensitivity), granularity, and relevance ranking capabilities that many typical but complex research queries have. One of the most popular demands that general-purpose systems such as PubMed fail to satisfy is the ability to extract and compile specific knowledge or facts out of literature records. For example, there is no provision in PubMed-like systems to determine which genes have been studied thus far in relation to a certain type of malignancy, other than to read through the set of articles identified by PubMed using keywords defining the concepts “gene” and “cancer” (or the type of cancer of interest), and then identifying the particular genes one article at a time. With the exponentially increasing literature size, the process will not only be more time consuming, but also be less reliable on getting the right articles. Consequently, the gap between what is recognized and what is currently known is widening (Wren JD et al, 2004). Biomedical text mining techniques can help researchers meet this challenge by developing automated systems to extract the relevant information out of the text and organize it into a structured knowledgebase. 2

Data Integration Opportunities in Cancer Research The general challenge of biomedical literature knowledge extraction is confounded in cancer research, including an acute need to more systematically identify linkages between genomic data and malignant phenotypes. Characterization of the molecular aberrations responsible for the onset and progression of malignancy is a major goal for cancer researchers, and genomic components of the aberrations, ranging from base pair variance to chromosome deletion, are crucial determinants in this regard. Despite the existence of some locus-, mutation- and disease-specific resources, there is currently no central cancer knowledge database in the public domain integrating genomic findings with phenotypic observations of tumors (Cairns J et al, 2000; Freimer N et al, 2003). While high-throughput screening efforts increasing allow researchers to identify genome-wide mutational profiles for specific tumors, this information is largely diffusely distributed and is mostly catalogued in a semi-structured manner throughout the biomedical literature. Such decentralization is holding back the efforts towards making rapid and comprehensive inferences of the genomic basis of malignancy onset and progression in a manner that incorporates cumulative knowledge. Ideally, researchers and clinicians would likely benefit from a comprehensive cancer knowledgebase that consolidates experimental work (genome-level investigation), clinical observations (descriptions of phenotype) and patient outcome (efficacy of treatment). Because the biomedical literature represents a large proportion of this information, which is both critically reviewed and eventually objective in its presentation of cancer research information, means for more adequately extracting, normalizing and relating such diverse collections of information in literature are crucial to solving this data integration problem 3

in cancer research. Named Entity Recognition The successful development of text mining technology has been increasingly applied in biomedical research to assist with meeting the above-mentioned challenges. There have been significant efforts from both computational linguists and bioinformaticists within the past 5 years to develop automated biomedical text mining (BTM) systems (Jensen LJ et al, 2006). BTM tasks include named entity recognition (NER), information extraction (IE), document retrieval (DR), and literature-based discovery (LBD). NER, which serves as the basis for most other BTM undertakings, is the process of identifying mentions of biomedical entities (objects, such as genes and diseases) in the text. Named entity recognition can be at first deceptively straightforward, but it is has emerged as a challenging and considerable task in BTM research. NER begins with the classification and definition of biomedical entities, which easily consumes tremendous amount of effort because of the complex and lack-of-standard nature in biomedical entities. The process of identifying references to biomedical objects in text is usually split into two steps: the identification of mentions of specific entity instances in text, such as “the p53 gene” or “acute lymphoblastic leukemia”; and the assignment of these mentions to a standard referent (normalization), such as classifying “the p53 gene” as a mention of the official gene symbol “TP53”, or “ALL” as “acute lymphoblastic leukemia”. Many biomedical entities either lack controlled vocabularies that can act as sufficient nomenclature standards, or the instances in text are not expressed with the standards due to historical reasons. Therefore, normalization is absolutely necessary for equating entity 4

values as appropriate, or placing values into a hierarchical or ontological framework (e.g., “ALL” as a form of “leukemia”. Much BTM research to date has focused upon molecular entities that tend to be more discretely definable, such as genes and protein-protein interactions, than phenotypic entities, which are harder to classify semantically (BioCreAtIvE; McDonald R et al, 2005; Settles BA 2005; Zhou G et al, 2005). NER methods include both rule-based and machine-learning approaches. Rule- based approaches use sets of “rules”, alone or in combination, that pre-state signature grammatical and especially character and word-based patterns within a string of text being considered, and then return Boolean values as an output. For example, a rule to identify a gene name could be “This word is a gene if it contains the consecutive letters ‘KIAA”, all of which are capitalized”. There can be some allowance for lexical variations, such as capitalization, stemming, or punctuation, and some or all rules might compare the text being considered to a term list, such as a pre-compiled list of known tumor types. However, the performance of the approach can’t count on the completion of the dictionary-type list in terms of both depth (the completion of the entity unique identifiers) and breadth (the completion of the synonyms for each unique identifier) because for most biomedical entities, the term lists are always changing and never complete. For complexly formulated text, rule-based approaches typically require considerable thought and exquisite biological knowledge. Advantages of this approach are relatively high precision without the requirement for generating extensive training material. However, disadvantages include high false negative rates, a performance plateau that is increasingly difficult to overcome, and, for complex and heterogeneous text, a tendency to generate low recall. Most first-generation systems and many domain- 5

focused current systems utilize rule-based approaches; when coupled with a term list, this approach accomplishes both steps of the overall NER task at one time. However, rule- based systems have enjoyed only modest success for biomedical applications, likely because their performances have plateaued below rates acceptable for wide use by researchers, or their application domains have been overtly narrow (Hanisch D et al, 2005; Fundel K et al, 2005; Chang JT et al, 2004; Finkel J et al, 2005). Given the limitations of rule-based systems, a number of machine-learning algorithms have been applied to improve the first step of the NER task. Generally, these algorithms consider and then define sets of features within and surrounding entity mentions that co-associate with the mentions. These can include orthographic features of the text (e.g., suffixes, particular sequential combinations of characters or words, capitalization patterns, etc.) and domain-specific features (e.g., term lists). For example, the suffix “-ase” usually indicates a protein name, and the noun phrase immediately preceding the word “gene” is often a gene name. Machine-learning approaches have several advantages: at their purest, they require no domain knowledge; they can consider thousands or millions of features simultaneously; they can provide confidence scores for predictions; and they can consider the entire feature space simultaneously. However, the success of machine-learning approaches is dependent upon two critical and costly factors. First, ML systems require the establishment, quality, and representativeness of a set of manually generated training material from which to “learn” features, a process that requires considerable effort and does not generalize effectively. Second, the most effective systems incorporate biological knowledge—either in the form of domain- specific rules or definition of features that are domain-specific (such as specialized 6

lexicons)—that are likewise costly to implement (McDonald R et al, 2004; Coller N et al, 2000; Tanabe L et al, 2002). It is most critical to let human set the examples of gold standards before machines can learn from it. To better reduce the annotation ambiguity and disagreement, it is crucial to define the target biomedical entities explicitly. Currently, most developed NER systems take some version of pre-established conceptual definitions, by which annotators could apply with very different standards. We have tried otherwise and put tremendous effort in an iterative annotation process to develop literature-based definitions drawing both the conceptual and textual boundaries. Step 2 work (normalization) is syntactically easier since the identification of textual boundaries is not necessary. However, it poses significant semantic challenges, because the non-unique synonyms have to be disambiguated to find out the real intent. And also, a comprehensive thesaurus like dictionary is necessary in order to match the raw entity mentions to their unique identifiers. Classification techniques, rule-based systems, and pattern-matching algorithms have been utilized to solve this issue, and some approaches also take the contextual information to disambiguate the synonyms (Chen L et al, 2005). Information Extraction Ideally, BTM systems extract and synthesize “facts” out of the literature that combine entity mentions with relationships between and among the mentions established in the literature. This work requires NER results, that is, the relationships between the entities can only be extracted once the individual entities have been identified. Although biomedically oriented research in this area is not as advanced as NER, BTM researchers 7

have recently been increasing their efforts on these challenges. A most straightforward but powerful approach is co-occurrence. This approach identifies the relationships between the involved biomedical entities based on their co- occurrence in the articles, or by considering how close mentions are to each other within a document. The assumption taken by the co-occurrence method is that if two (or more) entity instances are co-mentioned in one single text record (or defined subset, such as a sentence or a paragraph), these instances have some type of underlying biological relationship. As it is possible that entity instances can coincidentally co-occur, systems commonly use some parameters to rank the relationships, such as the frequency and location of their co-occurrence. If two entity instances are repeatedly co-mentioned together in close proximity, it is most likely that they are related. This approach tends to perform with better recall but at the expense of precision because it has no intelligent means for distinguishing specific from general relationships. For example, if the information to be extracted is the causal relationship between gene A and disease diagnostic labels, this approach will recognize relationships of any kind between gene A and relevant diseases, including but not limited to direct or causal relationships. In order to improve precision, some co-occurrence-based IE systems include additional approaches, such as combining with a customized text-categorization system to preferentially identify relevant articles or sentences. Co-occurrence-based IE systems are usually used as exploratory tools making inferential calls since they can identify both direct and indirect relationships between entity instances (Jessen TK et al, 2001; Alako BT et al, 2005). Another approach is to take advantage of natural language processing (NLP) 8

methodology that combines syntactic and semantic analysis of text. In this approach, individual tokens in test are often first identified and then assigned part-of-speech labels, in a process that has been converted to automation with high accuracy. Then a nested tree like structure (either top-down or bottom-up) is developed in order to determine the relationships between noun phrases or beyond, such as subjective and objective. After a NER process is applied for assigning semantic labels to specific words and phrases, either rule-based or machine-learning based processes can be used to extract relationships between entity mentions. Although the syntactic parsing and the semantic labeling have been carried out as separate steps by most NLP-based IE systems, results indicate that better performance can be obtained by integrating the two steps, due in part to the often complex relationships of biomedical entity mentions. This NLP-based approach can achieve better precision, but lower recall, largely because of increased challenges in identifying relationships across sentences. These approaches are also labor-intensive, since either expert defined sophisticated extraction rules or manually annotated training corpus are required (Rzhetsky A et al, 2004; Daraselia N et al, 2004; Yakushiji A et al, 2001). Although there is some research touching base with n-ary relationships between a set of biomedical entities, most IE systems currently classify binary relationships between same-type entities. These systems most commonly focus on entities and relationships that are easier to define, such as protein-protein/gene-protein interactions, protein phosphorylation, other specific relations between genomic entities such as cellular localizations of proteins, or interactions between proteins and chemicals. Few NER systems have yet to be designed for relating phenotypic attributes, such as gene-disease 9

relationships (Temkin et al, 2003; McDonald R et al, 2005). High-performance systems that can extract many types of relationships and also distinguish among relationships beyond the sentence level are not yet achievable. This is due largely to three contributing factors. First, biomedical text is complex and highly variable in its structure and presentation. Second, many complicating factors need to be considered, including co-reference (e.g, the use of pronouns), ambiguity in intent, and variability in formulation. Finally, systems need to incorporate various approaches simultaneously (e.g., tokenizers, POS taggers, NER systerms, parsers, disambiguators), each of which contributes some measure of error that combines to significantly degrade finalized output (Ding J et al, 2002). Document Retrieval DR systems typically identify and rank documents pertaining to a certain topic from a large collection of text. Topics of interest might be derived from user-supplied search terms or from pre-selecting specified types of documents. Most DR systems feature keyword search capabilities; advanced keyword searching allows users to input a combination of search terms and/or to perform advanced functions, such as including logical operations or inducing limits to terms. Systems then commonly retrieve documents containing or excluding certain terms that match the search criteria. This method often retrieves irrelevant articles, and relevance-ranking functions are often absent or primitive. More sophisticated DR systems go beyond this by applying distance metrics, such as a vector-space model. With this model, every document is represented as a vector, which is determined by measuring text-based features and/or document metadata, such as a list of frequency-based weighted terms identified in each document. 10

The query vector, which is determined by the relative importance of each query term, is then compared to document vectors to relevance rank the documents. The comparison between document vectors can also calculate document similarity. PubMed is a well- known DR system that is highly adapted for use as a query interface for MEDLINE. PubMed uses both keyword searching and a vector model (Glenisson P et al, 2003). Advanced DR systems integrate NER or other NLP methods in order to more accurately assess document content and identify documents that mention certain biomedical entity mentions. FABLE, MedMiner and Textpresso are examples of systems that make retrieval decisions by extracting and considering knowledge from gene/protein mentions in the documents (FABLE; Tanabe L et al, 1999; Muller HM et al, 2004). Literature-Based Discovery An ultimate goal of BTM is to assist with literature-based discovery. LBD can be defined as a process that discovers testable novel hypotheses by inferring implicit knowledge in biomedical literature. An early and often-cited example of LBD was from researcher recognizance of facts from two unrelated bodies of biomedical text, describing Raynaud’s disease, in which patients suffer from vasoconstriction, high blood viscosity and platelet aggregability, and describing fish oil, indicating that besides its capability of causing vasodilation, its active ingredient can also lower blood viscosity and platelet aggregation. This connection was formed completely through extensive reading of the literature, and later the relationship was proved experimentally. The model used in this seminal example was very simple: if A leads to B, and B leads to C, then it is plausible that A could lead to C. Based on this closed discovery process (to connect two previously known relations), this researcher subsequently discovered a novel association between 11

migraine and magnesium deficiency (also proved experimentally) as well as additional successes (Swanson DR 1986; Swanson DR 1988; Swanson DR 1990). More challenging LBDs might arise from an open discovery process, which attempts to derive relationships between two entities of interest through implicit relationships in literature. For example, the process of identifying candidate genes for a certain disease is an open discovery process. One example of this process would be to first identify gene mentions co-occurring in the literature (gene set A) with mentions of a disease of interest, next identifiying co-occurring gene mentions (gene set B) with known disease genes, and then consider the overlap between the two sets of gene mentions as candidate genes for the disease. There are two assumptions taken for this approach: Gene set B is functionally related with known disease genes; Gene set A has some sort of relations with the disease. One potential problem for this approach is that there are many types of direct and indirect relationships identified in such a process, including the high likelihood that a substantial number of false positives are generated. NLP-based IE can certainly help narrow down the relationship types, but further research is needed to improve the performance of such models. Also fundamentally, literature inevitably contains conflicting and inaccurate statements, which is impossible for an automated algorithm to adjudicate (Weeber M et al, 2005). It is much likely that more reliable inference of novel hypotheses and research directions from literature achieves success by integration of BTM results with other data types, including from curated data sets and experimental data. Experts’ curation and experimental evidence provides verification, filtering, and relevance ranking capabilities from information derived from real biological relationships between entities. For 12

example, researchers have made novel discoveries by transferring text-mined relationships of a protein to its orthologous proteins based on sequence-similarity searches. The integration effort of BTM results with functional genomic data such as microarray data has helped researchers rank significant genes as well as develop novel hypotheses based on both experimental data and previously known knowledge in a large scale, automated fashion (Yandell MD et al, 2002; Raychaudhuri S et al, 2002; Glenisson P et al, 2004). Significance Along with the rapid expanding of experimental data, the exponential increase of the biomedical research text makes it more and more difficult for researchers to track and utilize the relevant information to their interests, especially for the domains outside their core competence. Automated text mining systems can process the unstructured information in the literature into structured, queryable knowledgebase. This dissertation research has developed well-performed automated entity extractors based on the refined manual annotation with iteratively defined literature-based entity definitions in genomic variation of malignancy. Co-occurrence-based information extraction process was applied to integrate with microarray expression data in the pursuit of determining neuroblastoma research candidate genes. Both functional pathway analysis and RT-PCR experiment validated the text mining’s contribution. This thesis demonstrated that in addition to systematic curation of the textual information, biomedical text mining also has inferential capability especially when combined with experimental data. 13

Introduction to the Thesis Using the genomics of malignancy as a test bed, this thesis has touched upon every aspect of BTM outlined above. Work regarding the BTM process developed and employed will be discussed in detail in Chapter 2 and Chapter 3. This thesis has also established important work regarding information extraction in this domain, which has been applied to research regarding the pediatric tumor neuroblastoma (Chapter 3 and Chapter 4). Integration of BTM-extracted information with expression array analytical results to discover candidate genes for neuroblastoma research will be discussed in detail in Chapter 4. 14

Chapter 2. Defining Biomedical Entities for Named Entity Recognition Yang Jin Mark A. Mandel Peter S. White Abstract The performance of machine-learning based named entity recognition is highly dependent upon the quality of the training data, which is commonly generated by manual annotation of biomedical text representative of the target domain. The development of robust definitions of biomedical entities of interest is crucial for highly accurate recognition but is often neglected by text-mining applications. While the conceptual and syntactic complexities of biomedical entities often generate ambiguities in assigning text mentions to particular entity classes, entity definitions that exhibit as distinct semantic and textual boundaries as possible are desired. We have created a highly generalizable process for developing entity definitions specifying both conceptual limits and detailed textual ranges for target biomedical entities. This process utilizes representative text and manual annotators to initially define and iteratively refine definitions. The process was tested within the knowledge domain of genomic variation of malignancy. This work describes in detail the different types of challenges faced and the corresponding solutions devised during the definition process. The resulting entity definitions were used to annotate a training corpus for the development of automated entity extraction algorithms and for use by the research community. We conclude that manual annotation consistency is useful for the success of later biomedical text mining tasks, and that explicit, boundary- defined entity definitions can assist with achieving this goal. 15

1. Introduction Automated information extraction techniques can assist in the acquisition, management and curation of data. A necessary first step is the ability to automatically recognize biomedical entities in text, as also known as named entity recognition (NER). Development of named entity extractors for biomedical literature has progressed rapidly in recent years. For example, a number of machine-learning algorithms currently exist for identifying gene name instances in text (Collier N et al, 2000; Tanabe L et al, 2002; GENIA; Hanisch D et al, 2005). However, a major shortcoming of many approaches is that they often minimize efforts to define biomedical entities in an explicit fashion. Rather, the tendency is often to ignore this step by adapting or refining existing semantic standards as the target entities’ conceptual definitions, leaving interpretive details to manual annotators. Additionally, existing standards often provide little or none of the semantic depth required to establish concept boundaries with enough rigidity to provide highly accurate extraction. This tends to create outstanding consistency problems in later steps when training automated extractors and utilizing the extracted entity mentions for particular applications, because non-literature based conceptual definitions often generate significant annotation ambiguity problems due to the semantic as well as syntactic complexities of biomedical entities in the literature. As a result, automated systems derived from such systems tend to perform more poorly. For biologists in particular, high precision is a necessary prerequisite for widespread acceptance of automated tools, in order to establish a level of reliability acceptable to users. Strongly believing the importance of establishing well-defined, literature-based entity definitions with clear boundaries specially designed for biomedical NER practice, 16

the Biomedical Information Extraction Group at University of Pennsylvania (Penn BioIE) has developed an iterative annotation process designed to establish a set of “precise” entity definitions. These definitions are meant to clarify the conceptual boundaries both semantically and syntactically, while also striking a balance between the requirements of researchers, annotators, and computational scientists. This paper will first describe the annotation process developed by the Penn BioIE group, and then introduce the necessities and challenges of defining biomedical entities with specific examples in the literature. 2. Overview of manual annotation process and entity classification QuickTimeª and a TIFF (Uncompressed) decompressor are needed to see this picture. Figure 2-1. The processes of developing entity definitions and extractors Figure 2-1 demonstrates the iterative process developed for establishing and refining entity definitions, first through manual annotations and then in developing extractors based on the manually annotated training data. The process begins with the creation of an initial definition that establishes the general concept and scope of an entity 17

class, which is supplied by one or a group of domain experts. Commonly existing standards and resources are explored and, if deemed suitable, adopted as nuclei for the process. Subsequently, the domain expert(s) plays the role of adjudicating definition discrepancies. Manual annotators are then trained with the initial versions of the entity definitions, from which they manually annotate the selected training corpora. Invariably, as the annotators encounter the wide diversity of semantic representations of specific concepts, a need for iterative refinement of the entity definitions emerges. Often, text encounters require major revisions or even restructuring of definitions to accommodate such heterogeneity. Accordingly, definitions are continually refined during the analysis of annotated texts and annotation disambiguation. The Penn BioIE group founded useful frequent communication forums where the emerging definitions and identified exceptions were fully discussed among annotators and researchers. Communication modalities included weekly face-to-face meetings, email lists, and live chat. After annotation has been executed, entity extractors were developed by implementation of machine-learning algorithms utilizing probability models (we used Conditional Random Fields); the manually annotated texts were utilized as both training and testing data for these algorithms. Comparison of the annotations produced by the automatic extractors and human annotators allows for evaluation of the extractor performance. The target knowledge domain we chose was “Genomic Variation of Malignancy”, conceptualized as a relationship among three entity classes: Gene, Variation and Malignancy. As shown in Figure 2-2, the Gene and Variation entities comprise genomic components of cancer while the Malignancy entity covers phenotypic aspects of malignancy, including malignancy diagnostic labels and a number of malignancy 18

phenotypic attributes. QuickTimeª and a TIFF (Uncompressed) decompressor are needed to see this picture. Figure 2-2. Entity classification scheme for the domain of genomic variation of malignancy A total of 1442 MEDLINE abstracts were selected for exploration and annotation in this study, one subset of which contained many different malignancy types to establish breadth, and a second subset of which mentioned only one major malignancy (neuroblastoma) to establish depth. As diagrammed in Figure 2-1, the manual annotation process was first applied to the corpus with an electronic annotation tool, WordFreak (http://sourceforge.net/projects/wordfreak). After the entity definitions were refined and stabilized, the manually annotated data were then used to develop entity and attribute extractors (McDonald RT et al, 2004, Jin Y et al, 2006). These automated extractors 19

performed with state-of-the-art accuracy, in part due to the careful design and management of our annotation process. In the following paragraphs, we will discuss the challenges we have encountered during the manual annotation process, and why we believe that consistent entity definitions are critical for the success of later steps in biomedical text mining. 3. The challenges of defining biomedical entities Although we began this task believing we had clear ideas of what information each entity should cover, it quickly proved challenging to develop detailed working definitions. Our a priori notions of entity definition adequacy were that definitions establish distinct and defensible boundaries both conceptually and textually, therefore providing guidance to the annotators both semantically and syntactically. Solid entity definitions are an essential foundation for the subsequent steps of developing machine- learning algorithms and utilizing the extracted information for specific applications. First, the performance of entity extractors is highly dependent not only on the selection of the underlying algorithms, but also on the quality of the training data, which are entirely based on the entity definitions. If the annotators cannot identify specific entity mentions consistently on the basis of the definitions, it is hard to imagine that automated extractors can replicate this task reliably. More importantly, without clear definitions, researchers will certainly run into problems when trying to utilize the extracted mentions, since it will be difficult to know the precise boundaries of the gathered information. As mentioned earlier, we initially defined three major entities in the knowledge domain of genomic variation of malignancy, based on existing ontological categories and concepts. However, we quickly found that ontology-based definitions often don’t 20

precisely reflect what has been conceptualized throughout the biomedical texts contributed by researchers worldwide. For example, a gene defined by NCI thesaurus is: “A functional unit of heredity which occupies a specific position (locus) on a particular chromosome, is capable of reproducing itself exactly at each cell division, and directs the formation of a protein or other product.” If annotators use this definition for identifying gene mentions in the text, they could quickly be confused by many situations such as whether promoters should be included; how should gene family names be treated; how about pronoun referents to genes, etc. Thus, we found the need to invoke text-based working entity definitions, which are most effectively determined as annotators proceeded with the entity recognition task in the training corpus. Every new mention of an entity and every new context for a mention provided a test for the pre-developed entity definition. If a definition could not explicitly lead the annotators to a “correct”, or at least consistent decision in each case, the problematic mention required further examination, interpretation, and possibly, refinement of the definition. Through such an iterative process, we were able to develop fine-tuned entity definitions that provided distinct boundaries both for semantic scope and contextual range. The challenges that we encountered in refining our definitions can be grouped into four categories: conceptual, syntactic, syntactic/semantic ambiguity, and inter- annotator agreement. In the following paragraphs we will illustrate these types and give examples of our devised solutions and their limits. 3.1 Conceptual definition challenges As discussed earlier, an entity definition has to clarify both conceptual and textual boundaries. Initial versions of our definitions were completely conceptual, based on our 21

understanding of biomedical categories. Surprisingly, more than half of the annotators’ difficulties with definitions fell into this category during the annotation process, and most of them were reasonable as you can observe in the following paragraphs showing the four most common challenges in this category. This reflects the semantic complexity and diversity of biomedical entities, which often cannot be easily defined without some ambiguity. 3.1.1 Sub-classification of entities Based on the classification scheme stated above, our target knowledge domain was initially divided into three major conceptual classes: gene, genomic variation, and malignancy. However, this broad conceptual classification was far from sufficient for the generation of highly accurate extractors. For example, according to the conceptual definition, the malignancy concept covers all phenotypic information of cancer, including a tumor’s diagnostic type, the tumor’s anatomical location and cellular composition, and its differentiation status. Each of these types of information are presented in a variable and often bewildering array of syntactic and contextual patterns, which increases entropy and thus erodes the ability of machine-learning approaches to classify mentions. If instead we further classified the mentions into sub-categories such as those described above and annotated them as such, entropy is reduced and extractor performance can be expected to improve. However, a major disadvantage of this approach is that, sub- categorization introduces considerable additional annotation effort. Thus, the annotation process requires first the establishment of a level of entity granularity that balances the cost of manual annotation with the application value of the extracted data. There are countless ways to further divide entities into their underlying 22

components. For our purpose, we decided to let the level of granularity be generated by the annotation process. By beginning with broad classes and subdividing them as needed, we considered that we would eventually approach an optimal balance between effort and effectiveness. We considered it to be critical to determine how the text strings represented subcategories in the real world of biomedical literature. Therefore we divided our annotation efforts into two stages: data gathering and data classification, as demonstrated in Figure 2-3 with a genomic variation entity example. QuickTimeª and a TIFF (Uncompressed) decompressor are needed to see this picture. Figure 2-3. The text-based two-stage entity sub-classification process In the example illustrated by Figure 2-3, annotation of our initial concept of “Genomic Variation” proceeded through a preliminary stage of annotation before it was divided into sub-categories, which we named “Data Gathering”. In this stage, all textual mentions falling within or partially within our initial concept definition were annotated regardless of syntax. When sufficient information was gathered, sub-categories were 23

defined based on their semantic and syntactic representations. In addition, by proceeding with this exercise, the annotators became familiar with the concepts, definitions, and emerging challenges of the tasks. By employing this method, the sub-classification scheme began to approximate how the concepts were actually presented in the text. 3.1.2 Levels of specificity Textual entity mentions referring to the same semantic types can range from very general to quite specific, and not all levels of detail may be appropriate for a particular project. A gene mention may refer to a specific gene instance in a single cell of a sample, or to the wild type or a specific variation of the gene; or it may refer to gene families, super families and generalized classes, which represent classes of genes. For instance, “MAPK10” or “mitogen-activated protein kinase 10” is a family member of “MAPK”, which itself belongs to a higher level family “protein kinase”. We made the decision to include all levels of information for the gene entity except for the most general level such as “gene”. That is, in the above example, all three levels of gene mentions are legitimate and should be annotated as such. The decision was based on a couple of considerations. First of all, gene class information is valuable information to extract in later steps; although we don’t know which specific gene it refers to, it does help us narrow down to a class of genes. Second, if we only include the mentions describing genes at the instance level (the level that can lead to a specific genomic element), we have to draw a line between gene classes and instances. Because textual mentions for gene classes and instances are sometimes interchangeable (researchers tend to use gene class names referring to gene instance names and vice versa), it will be quite difficult for the automated extractors to distinguish 24

between the two. And finally, we exclude gene mentions at the most general level, which contains no information content or application value to extract. In another words, all information-containing levels of mentions are included. 3.1.3 Conceptual overlaps between entities An ideal entity classification scheme should result in independent information categories without any conceptual overlaps. Unfortunately, the subjective and adaptive nature of biological objects makes this ideal especially difficult to achieve, especially when defining two different but related entities. Even a basic concept such as “organism” is difficult to define when considering entities such as viruses and viroids, self-replicating machines with attributes necessary but not necessarily sufficient to qualify as life forms. Because our gene and genomic variation concepts both fall within the genomic domain and are closely associated, we were very careful to make a clear distinction. Eventually, our gene entity evolved to encompass solely the names of genes and their downstream products (i.e., RNAs and proteins), while the genomic variation entity covered specific descriptions of genomic element variations. Although our definitions of gene and genomic variation managed to eventually establish a reasonable boundary between them, for other entities, we found it sometimes impossible to avoid the conceptual overlapping problem. We encountered such problems when trying to make a clear division between the entity classes symptom and disease. The symptom entity was designed to capture subjective or objective evidence of disease, such as headache, diarrhea or hyperglycemia, while the disease entity captured specific pathological processes with a characteristic set of symptoms, such as Long QT Syndrome or lung cancer. As with most cases, the distinction is often clear to domain experts unless 25

considerable scrutiny is requested, as it appears to be simple common sense that these concepts represent two distinct and non-overlapping sets of information. However, when presented with the broad contextual variation in use and, often, semantic intent, it actually becomes quite difficult to draw a clear boundary between the two. We quickly found that many terms can be considered as both symptoms and diseases, depending both upon intent and the level of domain knowledge available. For example, “arrhythmia” itself is a disease entity mention, representing a pathological process, but it is usually used as a diagnostic label of a disease (symptom), such as long QT Syndrome. We certainly don’t want to have two entity types heavily overlapping with each other, since that will make the classification unnecessary. That is not the case for the symptom and disease entity types, and their overlapping mentions are less than approximately 10% overall. Most conceptually overlapping mentions cannot be put into either category without reading the text. We leave it to the annotators to determine authors’ intent based on the context and increasingly, they became quite good at minimizing the disagreement. 3.1.4 Domain-specific clarification As biological entities tend to be conceptually subjective, we often found it to be quite challenging and labor-intensive to establish consistent conceptual boundaries. The process of defining the gene entity is a good example to illustrate this challenge. Initially, we considered the task of defining a “gene” to be a straightforward task, as this concept is considered by biologists to be a rather discrete object. The HUGO Gene Nomenclature Committee (HUGO), the nomenclature body tasked with establishing official names for human genes, defines a gene as “a DNA segment that contributes to phenotype/function. In the absence of demonstrated function a gene may be characterized by sequence, 26

transcription or homology". On top of that, our gene entity is initially defined as the nominal reference to a gene or its downstream product in biomedical text. However, as annotations moved forward, annotators raised more and more questions, forcing us to make difficult determinations on the boundaries as illustrated below. An example of biological complexity is the many ways that a gene can contribute to phenotype. Typically, genes functionally impact biological processes through their downstream products, proteins. However, there are DNA segments on the genome which are able to affect phenotype by regulating how genes are expressed in particular biological contexts. Promoter and enhancer regions, which are distinct segments of DNA (often far) removed from the DNA segment that directly contributes to an RNA and/or protein product, are such example. These elements control whether and when the gene itself is expressed. Although biologists disagree whether promoters should be considered as genes or components of particular genes, annotators are required to make a decision on the gene entity boundary limits. In this case, we considered our application domain to be the most important determinant, as the main focus of our gene entity was to capture those “traditional genes” that could be directly and consistently associated with a protein. Thus, we limited our scope of genes to include only what we considered to be biologically functional DNA segments which are translated into protein products. There are many more cases that required further clarification of the gene entity conceptual definition, such as how to deal with segments and multiplexes of genes/RNAs/proteins. We realized that consistency was more valuable than trying to establish universal truth, the former of which we considered to be the key to developing well-performing automated extractors and increasing the application value of extracted 27

mentions. 3.2 Syntactic definition challenges Even with precise conceptual definitions, we found that guidelines needed be made regarding the textual boundaries of the entity mentions. Although many of these were syntactical nuances, they were not necessarily trivial for the annotator disagreement. In order to make consistent automated extractors, we determined that detailed annotation guidelines were required to make manual annotations consistent between different annotators. We designed our guidelines to be practical and based on actual contexts, specifying to the annotators exactly what to do under any uncertain circumstances that we had encountered. 3.2.1 Associating a text string to an entity mention There are many different ways to associate a text string with an entity mention in biomedical literature. In order to harvest consistent training data to develop highly performed automated extractors, we needed to define a series of rules specifying how to select text strings in the literature as legitimate entity mentions. We allowed entity references to include more than one word, including punctuation, but not to cross sentence boundaries. Although the majority of the entity mentions were nouns, not all of them were. For some entity mentions such as variation type, other part-of-speech forms were not uncommon. For example, for genomic variation types that would likely be normalized as the forms “insertion”, “deletion”, or “translocation”, those variation type mentions were usually expressed as verbs: “inserted”, “deleted”, or “translocated”. Moreover, malignancy attribute mentions were nearly always adjectives, such as “well- 28

differentiated”, “hereditary”, and “malignant”. All modifiers in a noun phrase mention were considered to be included as part of a mention, because not only can the modifiers provide very useful information to be extracted, but also that some modifiers are indispensable parts of the standard terms. We observed that this decision made it easier for both manual annotators and machine- learning extractors to operate since it was difficult to define boundaries on what modifiers to include in noun phrases. However, modifiers were not included for other part-of-speech phrases, in order not to complicate the issue. For example, in a noun phrase malignancy type mention “malignant squamous cell carcinoma”, both “malignant” and “squamous cell” are the modifiers of “carcinoma”, and both provide very useful information. “Squamous cell carcinoma” is also a commonly employed name of a type of cancer. Our experience determined that it was difficult for annotators and impossible for automatic extractors to draw consistent boundaries between modifiers on what should be included as part of the legitimate mentions. Lastly, we found it necessary to make entity-specific rules for some biological entities. For example, the gene entity mentions commonly appeared in the text as “The mycn gene…”, necessitating a decision as to whether the article “The” and the noun “gene” should be included as part of the entity mention. We reasoned that the decision should depend on how the extracted information was to be further processed and utilized. Accordingly, we decided to include neither word, since all the extracted gene mentions were to be subsequently mapped and normalized to official gene symbols. 3.2.2 Co-reference issue Often a single entity is referred to in different ways in the same text, a situation 29

known as co-reference. Besides its standardized form, an entity instance can also be referred to by aliases, acronyms, descriptions or pronoun references. For example, the mycn gene has at least 10 aliases in the literature, including “n-myc”, “oded”, and “v-myc avian myelocytomatosis viral related oncogene, neuroblastoma derived”. Moreover, researchers commonly engineer their own acronyms as self-convenient but non-standard and often unique aliases. Co-reference is generally recognized as a challenging task for entity recognition and information extraction. To deal with this issue in manual annotation, we have classified this problem into the following four categories and made corresponding decisions for each of them. A. Extended form vs. acronym Regular expression: ___ ___ ___ (___) Examples: • …mitogen-activated protein kinase (MAPK)…-- gene entity mention • …squamous cell carcinoma (SCC)… -- malignancy type entity mention Our decision: Tag both the extended form and abbreviated form of the entity mention. For the above examples, “MAPK” is co-referential with “mitogen-activated protein kinase”, and “SCC” is co-referential with “squamous cell carcinoma”. Both extended forms and acronyms would be tagged as corresponding entity instances in our system. Our rationale: Both forms are interchangeable descriptions of entity mentions, and they should be treated equally. B. Alias description Regular expression: …Y…X… or …Y (X)… Examples: 30

• TrkA (NTRK1)… • The N-myc gene, or MYCN… Our decision: NTRK1 and MYCN are official name designations of the TrkA and N-myc genes, and here they are being co-referenced accordingly. We decided to tag all different expression forms of the entity instances, including standard/official nomenclatures, aliases or descriptions. Like acronyms and their extended forms, these various names are also tagged individually: in the first example, we tagged “TrkA” and “NTRK1” separately and without the parentheses, not the combined string “TrkA (NTRK1)”. Our rationale: Researchers often use unofficial nomenclatures for entity mentions, so we can’t just annotate standard descriptions. However, they should be normalized later. C. General vs. specific Regular expression: X, a (the) Y… Examples: • C-Kit, a tyrosine kinase which plays an important role, … • K-Ras is an oncogene. The Ras gene… Our decision: In the examples above, the gene family name “Ras” and the superfamily name “tyrosine kinase” are used to co-refer to the gene family instances “K-Ras” and “C- Kit”. In such situations, our annotation guideline treated the general terms and more specific terms completely independently, regardless of the co-referential relationship between them. That is, depending on the conceptual definition, if the term was a legitimate mention, it was tagged as an entity mention no matter what levels of specificity it had. For those examples, since the gene entity definition included both gene instances and family names, all four terms were tagged as gene entity mentions. We did not, 31

however, tag “oncogene”, nor did we extend the tag on “Ras” to include the following word “gene”. These words, at the highest level of generality, convey no taggable information. Our rationale: Based on our decision on tagging all information-containing levels of mentions and specifically for the examples listed, all gene instances, gene families and superfamilies are determined legitimate mentions. D. Pronoun reference Regular expression: …X…PRONOUN (It, This, etc.)… Examples: • K-Ras is an oncogene. It is mutated in… • Five point mutations were found in the MYC gene, and they were next to each other. Our decision: In the two examples, “It” is co-referential to “K-Ras”, and “they” is co- referential to “point mutations”. We generally did not annotate pronouns, although they may refer to legitimate entity mentions. Our rationale: Pronoun co-reference is a challenging problem in text mining research, which involves cross-sentence, whole-record level of relation extraction. Without deeper parsing of the text, there is no value by extracting the pronoun itself. 3.2.3 Structural overlap between entity mentions Entities can overlap not only conceptually, but also literally, with their textual mentions in the literature. Annotation guidelines were developed for the following 32

situations: A. Entity within entity – tag within tag This refers to the situation that one entity mention is completely included in the textual range of another. As the two intertwined entity mentions could belong to either the same or different entities, we divided this category of problem into two sub- categories. If the two mentions were in the same entity, only the subsuming entity mention was tagged. For example, in “mitogen-activated protein kinase kinase kinase”, there exist 7 distinct gene entity mentions: mitogen-activated protein; mitogen-activated protein kinase; mitogen-activated protein kinase kinase; mitogen-activated protein kinase kinase kinase; and three mentions of “kinase”. While this type of a situation was a source of confusion among new annotators, we considered it both unnecessary and costly to tag all possible mention permutations. As the mention with the largest range was always the one being discussed, only the outermost mention was considered to be tagged as a gene mention. In fact, this situation led to the adoption of a more generalized guiding principle, where the annotation should reflect the author intent whenever possible (although exceptions were encountered, such as poorly written abstracts where the intent from the context occasionally and obviously differed from the actual word or phrase used). If two completely overlapping mentions instead belonged to different entity types, we annotated both. These mentions were usually related, and they both often provided valuable information. Some entities, such as malignancy attributes, often appeared as part of another entity mention. For instance, “colon cancer” is a malignancy type mention, and “colon” is a malignancy site mention. “Hirschsprung disease 1” is another example, that 33

“Hirschsprung disease” is a disease mention while the whole phrase is a gene mention. B. Entity co-identity – double tagging This category represents the situation that two entity mentions share the exact same text. We annotated the same text twice with the two corresponding labels under such circumstances. For example, in the phrase “deletion of the K-ras gene”, “K-ras” was tagged as both a gene entity mention and a variation-location mention. C. Discontinuous mentions – chaining Sometimes mentions of several entities of the same type shared a common substring. When written together in the text, the common part only occured once for the first or last mention, and other mentions were only represented with the different parts. For example, in the text “H-, K-, and N-ras…”, there are really three gene mentions: “H- ras”, “K-ras” and “N-ras”, but a limitation of our annotation software prevented tagging of discontinuous mentions as one parent mention (in the example above, only “N-ras” could be tagged. For the other two discontinuous mentions, we developed a chaining, procedure through which annotators were able to link the component parts (“H-” and “K-” with “ras”) by inserting comments into the annotation in a standard format. Chaining was strictly limited within one sentence in order not to complicate issues for subsequent syntactic parsing of sentences. Employing the same logic, entity mentions were not allowed to come across different sentences. 3.3 Syntactical vs. Semantic – ambiguity challenges We considered ambiguity in mentions to be the most common and difficult challenge in our annotation experience, as it truly reflects the limitation of human- invented texts in fully communicating author intent. In biomedical text, we found it not 34

uncommon that an identical text string could represent completely different concepts, and the frequency of ambiguity appeared to be much higher than for non-biological text. In the following paragraphs, we will use mainly gene entity examples to illustrate the illusive nature of this problem. We found ambiguity to occur both within and outside gene entities. Genes have a tradition of being independently named, with poor adherence to or awareness of standards. People tended to make up new acronyms for gene names, as the result of which, there are more gene names than the combinations of letters and numbers for short- character symbols/aliases. Thus, there are lots of similarities between aliases just by chance. Since each gene has multiple non-unique aliases with one unique gene symbol, there exists very serious internal ambiguity problem among the aliases. Based on our calculation, just for human genes alone, there are as many as 3% genes share the same aliases and the numbers are number higher if including other species. Also, many species have traditions of naming the genes the same, especially mouse and human (Chen L et al, 2005). For example, p90 is the common alias shared by the distinct gene symbols CANX and TFRC. As a protein naming convention, p90 actually refers to the protein with molecular weight 90. Therefore, it is not surprising that there are two proteins with the same name. When such gene mentions appear in literature, (often quite distant) context is the only way to clarify which gene is in discussion, although sometimes it offers no assistance. Another type of within gene entity ambiguity that we recognized was the frequent apparent inability to distinguish a gene from its downstream products, based purely on the text string of the mention. Although initially, our gene entity was designed 35

to capture only the nomenclatures of functional genomic elements, we soon discovered that researchers were frequently using the same referents to represent a gene and also its RNA and protein products in the literature. Without looking at the context, a gene mention “mycn” had almost an equal probability to refer to a gene or its downstream product, and both the gene and its mRNA were referred to as being “expressed” to create a mRNA or a protein product, respectively. In addition, authors also tended to obscure the conceptual boundaries between a gene and its downstream products. For example, while a given protein X performs biological functions, we found it common that the corresponding gene X was being described as performing this action. It became apparent that while researchers were personally clear regarding distinctions, their descriptions did not adequately convey these distinctions. In fact, in several cases, we found it impossible to determine whether certain gene mentions referred to a gene or its RNA or protein products even when considering the entire article. This overwhelming ambiguity problem finally prompted us to reach the decision to include genes’ downstream products when annotating gene entity mentions. Finally, we created one entity class gene but also included labels for partially subdividing them, while making considerations for not being able to perfectly divide mentions into the 3 classes. If it was not clear in the text whether a mention referred to a gene or a protein, the mention was annotated as “gene.generic”, as apposed to “gene.gene/RNA” or “gene.protein”. Besides the challenges mentioned above, it was common to encounter gene entity mentions that were easily be confused with objects belonging to other entity types, This is because genes have been named with a wide variety of methods, from the use of lay languages to the invention of specialized and often clever acronyms. For example, “Cat” 36

is an official gene symbol for the gene catalase, while it could also be used to refer to a kind of animal. “NB” is the acronym of a well-known pediatric cancer neuroblastoma, but it is also an official name of a gene locus putatively located on chromosome 1p36. This cross-entity ambiguity problem was also commonly seen for other entity classes, such as variation type. As an example, “Insertion” and “deletion” are well-defined variation type mentions, but they are also frequently used to denote biological or clinical actions. Regardless of the types of the ambiguity problems, the task for our manual annotators was to make their best calls to identify the intended reference of the text strings and annotate them as such. Sometimes annotators needed to take entire abstract or, rarely, the entire article, into consideration in order to determine what particular mentions truly represented. Depending on the nature of the biomedical entities and how representative the training data was, the subsequent automatic extractors were able to disambiguate problematic text strings to certain degree by taking local contextual features into account. 3.4 Annotator perceptions Even if perfect entity definitions and annotation guidelines could somehow be created, there would still be variations among human annotators in understanding and applying them during the annotation process, and we certainly encountered lively discussion regarding some topics. Usually, manual annotation is done by different annotators in order to get more files done within a shorter period of time, but the downside is that it introduces more inconsistencies between annotators. Even with only one annotator, there will be variability in application of guidelines. We took two approaches to deal with this problem. First, annotators were told to 37

discuss anything unclear, and we promoted frequent discussion to determine a consistent path. And also, a dual, sequential-pass manual annotation process was developed and applied to better adjudicate different annotators’ work and produce training data as consistent as possible. During this process, every document was annotated de novo by one annotator and then subsequently checked by a second annotator, who is more experienced and consistent, charged with identifying and revising any annotations considered to be incorrect by first pass annotators. Edited items were then subject to review by the group, and senior annotators used this editing process as an opportunity for educating less experienced annotators if repeated error patterns were identified. 3.5 Publication-based errors Typographical and grammatical errors, though infrequent, are inevitable, and some of them were observed in entity mentions during our process. Due to the considerations of copyright issues, we were not authorized to change the text in such cases but instead skipped tagging the mentions with added comments. 4. Application As a result of the generation and application of these carefully refined entity definitions and annotation guidelines, 1442 MEDLINE abstracts were manually annotated. Of these, 1157 files have been made publicly available (release 0.9, BioIE web site). Since the release, the data has been widely used by the biomedical text mining community for a variety of purposes, including entity recognition, normalization etc., and the usage is likely to increase (Cohen KB et al, 2005). Because of the consistency of the training data across the corpus, the developed entity and attribute extractors perform with high precision and recall rates. Table 2-1 38

indicates the performance of three entity extractors built with this data (McDonald RT et al, 2004; Jin Y et al, 2006). Entity Precision Recall F-measure Gene 0.864 0.787 0.824 Variation Type 0.8556 0.7990 0.8263 Location 0.8695 0.7722 0.8180 State-Initial 0.8430 0.8286 0.8357 State-Sub 0.8035 0.7809 0.7920 Overall 0.8541 0.7870 0.8192 Malignancy type 0.8456 0.8218 0.8335 Table 2-1: Entity extractor performance on evaluation data 5. Conclusion Manual annotation is an indispensable step to create training data for developing machine-learning automated extractors. In order to generate extractors that perform with accuracies high enough to be acceptable to the biomedical research community, consistently annotated training data is a prerequisite. Although we did not formally prove it, our experience has been that investment of developing literature-based entity definitions and annotation guidelines yields far better extracted information with distinct conceptual boundaries, which in turn increases the opportunity for practical application. We have concluded that rather than trying to construct unifying definitions that maximize acceptance and minimize contention amongst domain experts, that a consistent and generally arguable definition was preferable when making decisions to specify entity boundaries and magnitudes. More important for us was to consider how the extracted information will be used, and once determined, how to maintain consistency throughout the training corpus. 39

Reference Chen L, Liu H, Friedman C: Gene name ambiguity of eukaryotic nomenclatures. Bioinformatics, 21: 248-256. (2005). Cohen KB, Fox L, Ogren PV, Hunter L: Corpus design for biomedical natural language processing. Proceedings of the ACL-ISMB workshop on linking biological literature, ontologies and databases, pp. 38-45. Association for Computational Linguistics. (2005). Collier N, Nobata C, Tsujii J: Extracting the names of genes and gene products with a hidden Markov model. In Proceedings of the 18th International Conference on Computational Lingustics, Saarbrucken, Germany. (2000). GENIA: http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/ (2004). Hanisch D, Fundel K, Mevissen HT, Ximmer R, Fluck J: ProMiner: rule-based protein and gene entity recognition. BMC Bioinformatics. 6: S14. (2005). Jin Y, McDonald RT, Lerman K, Mandel MA, Carroll S, Liberman MY, Pereira FC, Winters RS, White PS: Automated recognition of malignancy mentions in biomedical literature. BMC Bioinformatics, 7: 492. (2006). McDonald RT, Winters RS, Mandel M, Jin Y, White PS, Pereira F: An entity tagger for recognizing acquired genomic variations in cancer literature. Bioinformatics 22(20): 40

3249-3251. (2004). Penn BioIE: http://bioie.ldc.upenn.edu/index.jsp Tanabe L, Wilbur W: Tagging gene and protein names in biomedical text, Bioinformatics, 18:1124-1132. (2002). 41

Chapter 3. Automated Recognition of Malignancy Mentions in Biomedical Literature Yang Jin Ryan T. McDonald Kevin Lerman Mark A. Mandel Steven Carroll Mark Y. Liberman Fernando C. N. Pereira R. Scott Winters Peter S. White Pulished: BMC Bioinformatics, 7:492, 2006 Abstract Background: The rapid proliferation of biomedical text makes it increasingly difficult for researchers to identify, synthesize, and utilize developed knowledge in their fields of interest. Automated information extraction procedures can assist in the acquisition and management of this knowledge. Previous efforts in biomedical text mining have focused primarily upon named entity recognition of well-defined molecular objects such as genes, but less work has been performed to identify disease-related objects and concepts. Furthermore, promise has been tempered by an inability to efficiently scale approaches in ways that minimize manual efforts and still perform with high accuracy. Here, we have applied a machine-learning approach previously successful for identifying molecular entities to a disease concept to determine if the underlying probabilistic model effectively generalizes to unrelated concepts with minimal manual intervention for model retraining. 42

Results: We developed a named entity recognizer (MTag), an entity tagger for recognizing clinical descriptions of malignancy presented in text. The application uses the machine-learning technique Conditional Random Fields with additional domain- specific features. MTag was tested with 1,010 training and 432 evaluation documents pertaining to cancer genomics. Overall, our experiments resulted in 0.85 precision, 0.83 recall, and 0.84 F-measure on the evaluation set. Compared with a baseline system using string matching of text with a neoplasm term list, MTag performed with a much higher recall rate (92.1% vs. 42.1% recall) and demonstrated the ability to learn new patterns. Application of MTag to all MEDLINE abstracts yielded the identification of 580,002 unique and 9,153,340 overall mentions of malignancy. Significantly, addition of an extensive lexicon of malignancy mentions as a feature set for extraction had minimal impact in performance. Conclusions: Together, these results suggest that the identification of disparate biomedical entity classes in free text may be achievable with high accuracy and only moderate additional effort for each new application domain. Background The biomedical literature collectively represents the acknowledged historical perception of biological and medical concepts, including findings pertaining to disease- related research. However, the rapid proliferation of this information makes it increasingly difficult for researchers and clinicians to peruse, query, and synthesize it for biomedical knowledge gain. Automated information extraction methods, which have recently been increasingly concentrated upon biomedical text, can assist in the acquisition and management of this data. Although text mining applications have been successful in 43

other domains and show promise for biomedical information extraction, issues of scalability impose significant impediments to broad use in biomedicine. Particular challenges for text mining include the requirement for highly specified extractors in order to generate accuracies sufficient for users; considerable effort by highly trained computer scientists with substantial input by biomedical domain experts to develop extractors; and a significant body of manually annotated text—with comparable effort in generating annotated corpora—for training machine-learning extractors. In addition, the high number and wide diversity of biomedical entity types, along with the high complexity of biomedical literature, makes auto-annotation of multiple biomedical entity classes a difficult and labor-intensive task. Most biomedical text mining efforts to date have focused upon molecular object (entity) classes, especially the identification of gene and protein names. Automated extractors for these tasks have improved considerably in the last few years [1-13]. We recently extended this focus to include genomic variations [14]. Although there have been efforts to apply automated entity recognition to the identification of phenotypic and disease objects [15-17], these systems are broadly focused and often do not perform as well as those utilizing more recently-evolved machine-learning techniques for such tasks as gene/protein name recognition. Recently, Skounakis and colleagues have applied a machine-learning algorithm to extract gene-disorder relations [18], while van Driel and co-workers have made attempts to extract phenotypic attributes from Online Mendelian Inheritance in Man [19]. However, more extensive work on medical entity class recognition is necessary because it is an important prerequisite for utilizing text information to link molecular and phenotypic observations, thus improving the 44

association between laboratory research and clinical applications described in the literature. In the current work, we explore scalability issues relating to entity extractor generality and development time, and also determine the feasibility of efficiently capturing disease descriptions. We first describe an algorithm for automatically recognizing a specific disease entity class: malignant disease labels. This algorithm, MTag, is based upon the probability model Conditional Random Fields (CRFs) that has been shown to perform with state-of-the-art accuracy for entity extraction tasks [5, 14]. CRF extractors consider a large number of syntactic and semantic features of text surrounding each putative mention [20, 21]. MTag was trained and evaluated on MEDLINE abstracts and compared with a baseline vocabulary matching method. An MTag output format that provides HTML-visualized markup of malignant mentions was developed. Finally, we applied MTag to the entire collection of MEDLINE abstracts to generate an annotated corpus and an extensive vocabulary of malignancy mentions. Results MTag performance Manually annotated text from a corpus of 1,442 MEDLINE abstracts was used to train and evaluate MTag. Abstracts were derived from a random sampling of two domains: articles pertaining to the pediatric tumor neuroblastoma and articles describing genomic alterations in a wide variety of malignancies. Two separate training experiments were performed, either with or without the inclusion of malignancy-specific features, which were the addition of a lexicon of malignancy mentions and a list of indicative suffixes. In each case, MTag was tested with the same randomly selected 1,010 training 45

documents and then evaluated with a separate set of 432 documents pertaining to cancer genomics. The extractor took approximately 6 hours to train on a 733 MHz PowerPC G4 with 1 GB SDRAM. Once trained, MTag can annotate a new abstract in a matter of seconds. For evaluation purposes, manual annotations were treated as gold-standard files (assuming 100% annotation accuracy). We first evaluated the MTag model with all biological feature sets included. Our experiments resulted in 0.846 precision, 0.831 recall, and 0.838 F-measure on the evaluation set. Additionally, the two subset corpora (neuroblastoma-specific and genome-specific) were tested separately. As expected, the extractor performed with higher accuracy with the more narrowly defined corpus (neuroblastoma) than with the corpus more representative for various malignancies (genome-specific). The neuroblastoma corpus performed with 0.88 precision, 0.87 recall, and 0.88 F-measure, while the genome-specific corpus performed with 0.77 precision, 0.69 recall, and 0.73 F-measure. These results likely reflect the increased challenge of identifying mentions of malignancy in a document set demonstrating a more diverse collection of mentions. To determine the impact of the biological feature sets we included to provide domain specificity, we excluded these feature sets to create a generic MTag. This extractor was then trained and evaluated using the identical set of files used to train the biological MTag version. Somewhat surprisingly, the extractor performed with similar accuracy with the generic model, resulting in 0.851 precision, 0.818 recall, and 0.834 F-measure on the evaluation set. These results suggested that at least for this class of entities, the extractor performs the task of identifying malignancy mentions efficiently without the 46

use of a specialized lexicon. Extraction versus string matching We next determined performance of MTag relative to a baseline system that could be easily employed. For the baseline system, the NCI neoplasm ontology, a term list of 5,555 malignancies, was used as a lexicon to identify malignancy mentions [22]. Lexicon terms were individually queried against text by case-insensitive exact string matching. A subset of 39 abstracts randomly selected from the testing set, which together contained 202 malignancy mentions, were used to compare the automated extractor and baseline results. MTag identified 190 of the 202 mentions correctly (94.1%), while the NCI list identified only 85 mentions (42.1%), all of which were also identified by the extractor. We also determined the performance of string matching that instead used the set of malignancy mentions identified in the manually curated training set annotations (1,010 documents) as a matching lexicon. This system identified 79 of 202 mentions (39.1%). Combining the manually-derived lexicon with the NCI lexicon yielded 124 of 202 matches (61.4%). A closer analysis of the 68 malignancy mentions missed by the string matching with combined lists but positively identified by MTag determined two general subclasses of additional malignant mentions. The majority of MTag-unique mentions were lexical or modified variations of malignancies present either in the training data or in the NCI lexicon, such as minor variations in spelling and form (e.g., “leukaemia” versus “leukemia”), and acronyms (e.g., “AML” in place of “acute myeloid leukemia”). More importantly, a substantial minority of mentions identified only by MTag were instances of the extractor determining new mentions of malignancies that were, in many cases, 47

neither obvious nor represented in readily available lexicons. For example, “temporal lobe benign capillary haemangioblastoma” and “parietal lobe ganglioglioma” are neither in the NCI list or training set per se, or approximated as such by a lexical variant. This suggests that MTag contributes a significant learning component. Application to MEDLINE MTag was then used to extract mentions of malignancy from all MEDLINE abstracts through 2005. Extraction took 1,642 CPU-hours (68.4 CPU-days; 2.44 days on our 28-CPU cluster) to process 15,433,668 documents. A total of 9,153,340 redundant mentions and 580,002 unique mentions (ignoring case) were identified. Interestingly, the ratio of unique new mentions identified relative to the number of abstracts analyzed was relatively uniform, ranging from a rate of 0.183 new mentions per abstract for the first 0.1% of documents to a rate of 0.038 new mentions per abstract for the last 1% of documents. This indicated that a substantial rate of new mentions was being maintained throughout the extraction process. The 25 mentions found in the greatest number of abstracts by MTag are listed in Table 1. Six of these malignant phrases: pulmonary, fibroblasts, neoplastic, neoplasm metastasis, extramural, and abdominal did not match our definition of malignancy. Of these, only “extramural” is not frequently associated with malignancy descriptions and is likely the result of containing character n-grams that are generally indicative of malignancy mentions. The remaining five phrases are likely the result of the extractor failing to properly define mention boundaries in certain cases (e.g., tagging “neoplasm” rather than “brain neoplasm”), or alternatively, shared use of an otherwise indicative character string (e.g., “opl” in “brain neoplasm” and “neoplastic”) between a true positive 48

and a false positive. For comparison, we also determined the corresponding number of articles identified both by keyword searching of PubMed and by exact string matching of MEDLINE for each of the 19 most common true malignancy types (Table 1). Overall, MTag’s comparative recall was 1.076 versus PubMed keyword searching and 0.814 versus string matching. As PubMed keyword searching uses concept mapping to relate keywords to related concepts, thus providing query expansion, the document retrieval totals derived from this approach do not strictly compare to MTag’s approach. Furthermore, the exact string totals would be inflated relative to the MTag totals, as for example the phrase “myeloid leukemia” would be counted both for this category and for a category “leukemia” with exact string matching, but would only be counted for the former phrase by MTag. To adjust for these discrepancies, for MTag document totals listed in Table 1, we included documents that were tagged with malignancy mentions that were both strict syntactic parents and biological children of the phrase used. For example, we included articles identified by MTag with the phrase “small-cell lung cancer” within the total for the phrase “lung cancer”. Comparison of these totals between MTag articles and PubMed keyword searching revealed that MTag provided high recall for most malignancies. Interestingly, there are three malignancy mention instances (“carcinoma”, “sarcoma”, “melanoma”) that have more MTag-identified articles than for PubMed keyword searches. This suggests that a more formalized normalization of MTag-derived mentions might assist both with efficiency and recall if employed in concert with the manual annotation procedure currently employed by MEDLINE. Furthermore, MTag’s document recall compared 49

quite favorably to exact string matching. Only two of the 25 malignancy mentions yielded less than 60% as many articles via MTag than via PubMed exact string matching (“bone neoplasms” and “lung cancer”). In these two cases, the concept-mapping PubMed search identifies the articles with a broader range beyond the search terms. For example, a PubMed search for the term “lung cancer” identifies articles describing “lung neoplasms”, while for “bone neoplams”, articles focusing on related concepts such as “osteoma” and “sphenoid meningioma” are identified by PubMed. Generally, MTag recall would be expected to improve further after a subsequent normalization process that maps equivalent phrases to a standard referent. To assess document-level precision, we randomly selected 100 abstracts identified by MTag each for the malignancies “breast cancer” and “adenocarcinoma”. Manual evaluation of these abstracts showed that all of the articles were directly describing the respective malignancies. Finally, we evaluated both the 250 most frequently mentioned malignancies as well as a random set of 250 extracted malignancy mentions from the all- MEDLINE-extracted set. For the frequently occurring mentions, 72.06% were considered to be true malignancies; this set corresponds to 0.043% of all malignancy mentions. For the random set, 78.93% were true malignancies. This suggests that such extracted mention sets might serve as a first-pass exhaustive lexicon of malignancy mentions. Comparison of the entire set of unique mentions with the NCI neoplasm list showed that 1,902 of the 5,555 NCI terms (34.2%) were represented in the extracted literature. Software 50

MTag is platform independent, written in java, and requires java 1.4.2 or higher to run. The software is freely available under the GNU General Public License at http://bioie.ldc.upenn.edu/index.jsp?page=soft_tools_MalignancyTaggers.html. MTag has been engineered to directly accept files downloaded from PubMed and formatted in MEDLINE format as input. MTag provides output options of text or HTML file versions of the extractor results. The text file repeats the input file with recognized malignancy mentions appended at the end of the file. The HTML file provides markup of the original abstract with color-highlighted malignancy mentions, as shown in Figure 1. Discussion We have adapted an entity extraction approach that has been shown to be successful for recognition of molecular biological entities and have shown that it also performs with high accuracy for disease labels. It is evident that an F-measure of 0.83 is not sufficient as a stand-alone approach for curation tasks, such as the de novo population of databases. However, such an approach provides highly enriched material for manual curators to utilize further. As was determined by our comparisons with lexical string matching and PubMed-based approaches, our extraction method demonstrated substantial improvement and efficiency over commonly employed methods for document retrieval. Furthermore, MTag appeared to be accurately predicting malignancy mentions by learning and exploiting syntactic patterns encountered in the training corpus. Analysis of mis-annotations would likely suggest additional features and/or heuristics that could boost performance considerably. For example, anatomical and histological descriptions were frequent among MTag false positive mentions. Incorporation of lexicons for these entity types as negative features within the MTag model would likely 51

increase precision. Our training set also does not include a substantial number of documents that do not contain mentions of malignancy; recent unpublished work from our group suggests that inclusion of such documents significantly impacts extractor performance in a positive manner. Unlike the first iteration of our CRF model [14], the MTag application required only modest computational effort (several weeks vs. several months) of retraining and customization time (see Methods). To our surprise, the addition of biological features, including an extensive lexicon for malignancy mentions, provided very little boost to the recall rate. This provides evidence that our general CRF model is flexible, broadly applicable, and if these results hold true for additional entity types, might lessen the need for creating highly specified extractors. In addition, the need for extensive domain- specific lexicons, which do not readily exist for many disease attributes, might be obviated. If so, one approach to comprehensive text mining of biomedical literature might be to employ a series of modular extractors, each of which is quickly generated and then trained for a particular entity or relation class. Conversely, it is important to note that the entity class of malignancy possesses a relatively discrete conceptualization relative to certain other phenotypic and disease concepts. Further adaptation of our extractor model for more variably described entity types, such as morphological and developmental descriptions of neoplasms, is underway. However, the finding that biological feature addition provided minimal gain in accuracy suggests that further improvements may be more difficult to obtain than by merely identifying and adding additional domain-specific features. Significantly, challenges in rapid generation of annotations for extractor training, as well as procedures for efficient and accurate entity normalization, still 52

remain. When combined with expert evaluation of output, extractors can assist with vocabulary building for targeted entity classes. To demonstrate feasibility, we extracted mentions of malignancy for all pre-2006 MEDLINE abstracts. Our results indicate that MTag can generate such a vocabulary readily and with moderate computational resources and expertise. With manual intervention, this list could be linked to the underlying literature records and also integrated with other ontological and database resources, such as the Gene Ontology, UMLS, caBIG, or tumor-specific databases [23-25]. Since normalization of disease-descriptive term lists requires considerable specialized expertise, the role of an extractor in this setting more appropriately serves as an information harvester. However, this role is important, as such supervised lists are often not readily available, due in part to the variability in which phenotypic and disease descriptions can be described, and in part to the lack of nomenclature standards in many cases. Finally, to our knowledge, MTag is one of the first directed efforts to automatically extract entity mentions in a disease-oriented domain with high accuracy. Therefore, applications such as MTag could contribute to the extraction and integration of unstructured, medically-oriented information, such as physician notes and physician- dictated letters to patients and practitioners. Future work will include determining how well similar extractors perform for identifying mentions of malignant attributes with greater (e.g. tumor histology) and lesser (e.g. tumor clinical stage) semantic and syntactic heterogeneity. 53

Conclusions MTag can automatically identify and extract mentions of malignancy with high accuracy from biomedical text. Generation of MTag required only moderate computational expertise, development time, and domain knowledge. MTag substantially outperformed information retrieval methods using specialized lexicons. MTag also demonstrated the ability to assist with the generation of a literature-based vocabulary for all neoplasm mentions, which is of benefit for data integration procedures requiring normalization of malignancy mentions. Parallel iteration of the core algorithm used for MTag could provide a means for more systematic annotation of unstructured text, involving the identification of many entity types; and application to phenotypic and medical classes of information. Methods Task definition Our task was to develop an automated method that would accurately identify and extract strings of text corresponding to a clinician’s or researcher’s reference to cancer (malignancy). Our definition of the extent of the label “malignancy” was generally the full noun phrase encompassing a mention of a cancer subtype, such that “neuroblastoma”, “localized neuroblastoma”, and “primary extracranial neuroblastoma” were considered to be distinct mentions of malignancy. Directly adjacent prepositional phrases, such as “cancer <of the lung>”, were not allowed, as these constructions often denoted ambiguity as to exact type. Within these confines, the task included identification of all variable descriptions of particular malignancies, such as the forms “squamous cell carcinoma” (histological observation) or “lung cancer” (anatomical location), both of which are 54

underspecified forms of “lung squamous cell carcinoma”. Our formal definition of the semantic type “malignancy” can be found at the Penn BioIE website [26]. Corpora In order to train and test the extractor with both depth and breadth of entity mention, we combined two corpora for testing. The first corpus concentrated upon a specific malignancy (neuroblastoma) and consisted of 1,000 randomly selected abstracts identified by querying PubMed with the query terms “neuroblastoma” and “gene”. The second corpus consisted of 600 abstracts previously selected as likely containing gene mutation instances for genes commonly mutated in a wide variety of malignancies. These sets were combined to create a single corpus of 1,442 abstracts, after eliminating 158 abstracts that appeared to be non-topical, had no abstract body, or were not written in English. This set was manually annotated for tokenization, part-of-speech assignments, and malignancy named entity recognition, the latter in strict adherence to our pre- established entity class definition [27, 28]. Sequential dual pass annotations were performed on all documents by experienced annotators with biomedical knowledge, and discrepancies were resolved through forum discussions. A total of 7,303 malignancy mentions were identified in the document set. These annotations are available in corpus release v0.9 from our BioIE website [29]. Algorithm Based on the manually annotated data, an automatic malignancy mention extractor (MTag) was developed using the probability model Conditional Random Fields (CRFs) [20]. We have previously demonstrated that this model yields state-of-the-art accuracy for recognition of molecular named entity classes [5, 14]. CRFs model the conditional 55

probability of a tag sequence given an observation sequence. We denote that O is an observation sequence, or a sequence of tokens in the text, and t is a corresponding tag sequence in which each tag labels the corresponding token with either Malignancy (meaning that the token is part of a malignancy mention) or Other. CRFs are log-linear models based on a set of feature functions, fi(tj, tj-1, O), which map predicates on observation/tag-transition pairs to binary values. As shown in the formula below, the function value is 1.0 when the tag sequence is Malignancy; otherwise (o.w.) it is 0. A particular advantage of this model is that it allows the effects of many potentially informative features to be simultaneously weighed. Consider, for example, the following feature: This feature represents the probability of whether the token “cancer” is tagged with label Malignancy given the presence of “lung” as the previous token. Features such as this would likely receive a high weight, as they represent informative associations between observation predicates and their corresponding labels. Our CRF algorithm considers many textual features when it makes decisions on classifying whether a word comprises all or part of a malignancy mention. Word-based features included whether a word has been identified as being a malignancy mention by manual annotation of text used as training material. The frequency of each string of 2, 3, or 4 adjacent characters (character n-grams) within each word of the training text was 56

calculated, and the differential frequency of each n-gram within words manually tagged as being malignancy mentions, relative to the overall frequency of these strings in the overall text, was considered as a series of features. Orthographic features included the usage and distribution of punctuation, alternative spellings, and case usage. Domain- specific features comprised a lexicon of 5,555 malignancies and a regular expression for tokens containing the suffix –oma. In total, MTag incorporated 80,294 unique features. All observation predicates, either with or without the biological predicates, were then applied over all labels, applying a token window of (-1, 1) to create the final set of features. The MALLET toolkit [30] was used as the implementation of CRFs to build our model. Evaluation The evaluation set of 432 abstracts comprised 2,031 sentences containing mentions of malignancy and 3,752 sentences without mentions, as determined by manual assessment of entity content. The predicted malignancy mention was considered correctly identified if, and only if, the predicted and manually labeled tags were exactly the same in content and both boundary determinations. The performance of MTag was calculated according to the following metrics: Precision (number of entities predicted correctly divided by the total number of entities predicted), Recall (number of entities predicted correctly divided by the total number of entities identified manually), and F-measure [(2*Precision*Recall)/(Precision+Recall)]. List of Abbreviations Used CRF, conditional random field 57

Authors’ contributions YJ implemented the algorithm to develop MTag and drafted the manuscript. RM developed the core algorithm and assisted in the implementation. KL developed the software interface. MM supervised the manual annotation for extractor training and testing. SC assisted with the tagging of MEDLINE and analysis of the results. ML oversaw the linguistic aspects of the project. FP developed the theoretical underpinnings of the algorithm and oversaw the computational aspects of the project. RW participated in algorithm design and the manual annotation procedure. PW oversaw the biological aspects of the project, provided overall direction, and finalized the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank members of the University of Pennsylvania Biomedical Information Extraction Group; Kevin Murphy for annotations, discussions and technical assistance; the National Library of Medicine for access to MEDLINE; and Richard Wooster for corpus provision. This work was supported in part by NSF grant ITR 0205448 (to ML), a pilot project grant from the Penn Genomics Institute (to PW), and the David Lawrence Altschuler Endowed Chair in Genomics and Computational Biology (to PW). 58

References 1. Collier N, Takeuchi K: Comparison of character-level and part of speech features for name recognition in biomedical texts. J Biomed Inform 2004, 37:423-435. 2. Finkel J, Dingare S, Manning CD, Nissim M, Alex B, Grover C: Exploring the boundaries: gene and protein identification in biomedical text. BMC Bioinformatics 2005, 6 Suppl 1:S5. 3. Hakenberg J, Bickel S, Plake C, Brefeld U, Zahn H, Faulstich L, Leser U, Scheffer T: Systematic feature evaluation for gene name recognition. BMC Bioinformatics 2005, 6 Suppl 1:S9. 4. Kinoshita S, Cohen KB, Ogren PV, Hunter L: BioCreAtIvE Task1A: entity identification with a stochastic tagger. BMC Bioinformatics 2005, 6 Suppl 1:S4. 5. McDonald R, Pereira F: Identifying gene and protein mentions in text using conditional random fields. BMC Bioinformatics 2005, 6 Suppl 1:S6. 6. Mitsumori T, Fation S, Murata M, Doi K, Doi H: Gene/protein name recognition based on support vector machine using dictionary as features. BMC Bioinformatics 2005, 6 Suppl 1:S8. 7. Tamames J: Text Detective: a rule-based system for gene annotation in biomedical texts. BMC Bioinformatics 2005, 6 Suppl 1:S10. 8. Tanabe L, Wilbur WJ: Tagging gene and protein names in biomedical text. Bioinformatics 2002, 18:1124-1132. 9. Tanabe L, Xie N, Thom LH, Matten W, Wilbur WJ: GENETAG: a tagged 59

corpus for gene/protein named entity recognition. BMC Bioinformatics 2005, 6 Suppl 1:S3. 10. Temkin JM, Gilder MR: Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics 2003, 19:2046-2053. 11. Torii M, Kamboj S, Vijay-Shanker K: Using name-internal and contextual features to classify biological terms. J Biomed Inform 2004, 37:498-511. 12. Yeh A, Morgan A, Colosimo M, Hirschman L: BioCreAtIvE Task 1A: gene mention finding evaluation. BMC Bioinformatics 2005, 6 Suppl 1:S2. 13. Zhou G, Shen D, Zhang J, Su J, Tan S: Recognition of protein/gene names from text using an ensemble of classifiers. BMC Bioinformatics 2005, 6 Suppl 1:S7. 14. McDonald RT, Winters RS, Mandel M, Jin Y, White PS, Pereira F: An entity tagger for recognizing acquired genomic variations in cancer literature. Bioinformatics 2004, 20:3249-3251. 15. Chen L, Friedman C: Extracting phenotypic information from the literature via natural language processing. Medinfo 2004, 11:758-762. 16. Friedman C, Hripcsak G, DuMouchel W, Hohnson SB, Clayton PD: Natural language processing in an operational clinical information system. Natural Language Engineering 1995, 1:1-28. 17. Hahn U, Romacker M, Schulz S: MEDSYNDIKATE--a natural language system for the extraction of medical information from findings reports. Int J Med Inform 2002, 67:63-74. 18. Skounakis M, Craven M, Ray S: Hierarchical Hidden Markov Models for 60

information extraction. Proceedings of the 18th International Joint Conference on Artificial Intelligence: 2003; Acapulco, Mexico; 2003. 19. van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA: A text- mining analysis of the human phenome. Eur J Hum Genet 2006, 14:535-542. 20. Lafferty J, McCallum A, Pereira F: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of ICML-01: 2001; 2001: 282-289. 21. McCallum A: Efficiently Inducing Features of Conditional Random Fields. UAI '03, Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence: 2003: Morgan Kaufmann; 2003: 403-410. 22. Malignancy type definitions [http://bioie.ldc.upenn.edu/mamandel/annotators/onco/definitions.html] 23. The Gene Ontology (GO) project in 2006. Nucleic Acids Res 2006, 34:D322-326. 24. Bodenreider O: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004, 32:D267-270. 25. Kakazu KK, Cheung LW, Lynne W: The Cancer Biomedical Informatics Grid (caBIG): pioneering an expansive network of information and tools for collaborative cancer research. Hawaii Med J 2004, 63:273-275. 26. Kulick S, Bies A, Liberman M, Mandel M, McDonald R, Palmer M, Schein A, Ungar L, Winters S, White P: Integrated annotation for biomedical information extraction. Proc of BioLink 2004 2004. 27. Kulick S, Liberman M, Palmer M, Schein A: Shallow semantic annotation of 61

biomedical corpora for information extraction. Proc ISMB 2003. 28. Penn BioIE corpus release v0.9 [http://bioie.ldc.upenn.edu] 29. MALLET: A Machine Learning for Language Toolkit [http://mallet.cs.umass.edu/] 30. Bruder E, Passera O, Harms D, Leuschner I, Ladanyi M, Argani P, Eble JN, Struckmann K, Schraml P, Moch H: Morphologic and molecular characterization of renal cell carcinoma in children and young adults. Am J Surg Pathol 2004, 28:1117-1132. 62

Table 3-1 MTag-identified PubMED keyword MEDLINE exact Evaluation MTag articles Mentions articles matches carcinoma True Positive 861214 466958 891996 breast neoplasms True Positive 129096 133592 137445 adenocarcinoma True Positive 166302 208117 183654 lung neoplasms True Positive 104176 110378 111869 pulmonary False Positive breast cancer True Positive 91446 147286 128381 lymphoma True Positive 182764 158674 226407 liver neoplasms True Positive 69513 84529 84712 fibroblasts False Positive skin neoplasms True Positive 62282 66072 66105 neoplastic False Positive neoplasm metastasis False Positive brain neoplasms True Positive 58729 84636 63586 stomach neoplasms True Positive 50019 52566 55208 prostatic neoplasms True Positive 48042 49110 50312 leukemia True Positive 163011 190798 368980 colonic neoplasms True Positive 41327 47402 42841 cervical neoplasms True Positive 40998 41424 41717 sarcoma True Positive 142665 110920 242654 bone neoplasms True Positive 33568 73429 35091 melanoma True Positive 79519 61134 126681 pancreatic neoplasms True Positive 31598 33775 33291 extramural False Positive lung cancer True Positive 53601 118679 66071 abdominal False Positive Table 3-1. Top 25 MTag identified mentions and their corresponding PubMED keywords and MEDLINE exact string matching search results. Figure 3-1 63

QuickTimeª and a TIFF (LZW) decompressor are needed to see this picture. Figure 3-1. Example of the HTML output of MTag for an annotated abstract [31]. Malignancy type mentions identified by MTag are shown in bold, italicized, and blue text. 64

Chapter 4. A Text Mining Approach for Identifying Genes Implicated in Neuroblastoma Tumorigenesis Yang Jin Jane Minturn Garrett M. Brodeur Peter S White Abstract The pediatric tumor neuroblastoma can be classified into two subtypes that commonly exhibit distinctly different clinical outcomes, and which appear to correlate with the differential activation of either the NTRK1 or NTRK2 neurotrophin signaling pathways. Previously, we generated neuroblastoma cell lines that constituitively express either the receptor tyrosine kinase NTRK1 or NTRK2 in an otherwise identical background. Microarray expression profiling of the cell line models after introduction of either NTRK1 ligand (NGF) or NTRK2 ligand (BDNF) gave rise to 751 genes differentially expressed between the two cell lines. We developed a method to re- prioritize the differentially expressed gene list by extracting and integrating information regarding genes differentially mentioned in biomedical text articles between NTRK1 and NTRK2, using a highly specific entity recognition and process. This process identified twenty-two genes differentially expressed and also differentially mentioned in the literature. The 22 genes were compared to the larger set of differentially expressed genes to determine the ability of each group’s genes to be enriched for protein pathways considered to be critical for neurolast development. Results demonstrated that text mining alone or when integrated with the microarray data was capable of further enriching the genes from the differentially expressed gene set. Expression levels for 11 of the 22 genes were verified by real-time expression analysis. One the eleven genes, EFNB3, validated 65

the biological utility of the text mining process, while another, TYRO3, suggested inferential power of the process. We conclude that biomedical text mining can help interpret high throughput data analysis by integrating previously known information. Introduction Neuroblastoma is the most common pediatric extracranial solid tumor, accounting for approximately 9% of all childhood cancers. Neuroblastoma is derived from primitive cells of the developing sympathetic nervous system. Progression of the disease is markedly variable, ranging from spontaneous regression of metastatic disease in a small minority of infants to metastatic disease that grows relentlessly, despite even the most intensive multimodality therapy, in many children over one year of age (Brodeur GM 2003). Based both upon these observations and a number of tumor classification studies using a wide range of biological and clinical factors, the presence of at least two biological subtypes with distinct clinical outcomes has been proposed. Previous studies have suggested that expression of the neurotrophin receptor NTRK1 (TrkA) is strongly correlated with favorable outcomes, while expression of NTRK2 (TrkB) conversely indicates an unfavorable outcome (Nakagawara A et al, 1992; 1993; 1994; Suzuki T et al, 1993; Kogner P et al, 1993; Borrello MG et al, 1993). The high binding-affinity ligands for NTRK1 and NTRK2 receptors are nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF) respectively. The NTRK1 and NTRK2 ligands, receptors, and, to the extent they are known, the downstream signal transduction pathways are highly similar in structure and composition. However, it has been well-established that the NGF/NTRK1 signaling pathway mediates cellular differentiation and/or programmed cell death in vitro, while the BDNF/NTRK2 pathway enhances neuroblastoma cell 66

survival (Eggert A et al, 2000; 2002; Ho et al, 2002). It is evident that these two signaling pathways must activate certain non-overlapping effector molecules and downstream targets, but the molecules that account for the distinct biological behaviors have not yet been elucidated. Therefore, further characterization of the differential molecular responders activated by the two similar neurotrophin signaling pathways might lead us to understand the mechanisms responsible for different phenotypic behaviors of the two neuroblastoma subtypes, as well as identifying possible clinical intervention targets. Array-based gene expression analysis is a recent, commonly employed, and increasingly effective strategy for identifying differentially active transcripts in a systematic fashion. However, array methods are well known to suffer from limited positive predictive value, due in part to the large number of genes being surveyed, and in part to limitations in the correlation between gene expression and biological activity. Although single-gene transcript surveillance systems such as real time PCR (RT-PCR) are more reliable ways to identify differentially expressed genes, as well as to validate array-based findings, employing these more sensitive techniques to identify more promising candidates is cost- and effort-prohibitive for most laboratories. Instead, researchers typically first undertake a high-throughput array-based screen and then select a small subset of the most differentially expressed genes for validation and further study. However, this process requires researchers to make subjective decisions that often rely on their own knowledge rather than more objective methods that consider additional knowledge sources regarding genes of interest for prioritization. Biomedical literature is the most complete and updated reservoir for discovered biomedical knowledge. While this knowledge source is immediately attractive, from an 67

information content standpoint, for discovery tasks such as the identification of genes implicated in human diseases, the unstructured nature of biomedical text obviates approaches to utilize this information for prioritization tasks systematically. However, biomedical text mining (BTM) techniques developed by us and others have recently demonstrated success in extracting target information out of text (Jin Y et al, 2006; McDonald RT et al, 2004; Rzhetsky A et al, 2004;Hanisch D et al, 2005; BioCreAtIvE). Effective use of such techniques could provide a large and structured data set of extracted information that would allow more comprehensive synthesis of published biomedical knowledge than current, ad hoc methods used by most researchers for literature awareness. However, BTM techniques are costly to implement and typically yield results that are inadequately sensitive if applied generally; thus, these systems have been slow to gain acceptance among biomedical researchers. In contrast, we and others have had considerable success constructing BTM applications that are limited in scope but are highly tuned to a particular practical task. With a previously developed named entity recognition (NER) system, we were able to identify human gene mentions in literature with high accuracy rates, normalize these to standard referents, and apply this system to the entire body of MEDLINE documents. In the current study, we applied this system to help address a particular biomedical research challenge, the identification of candidate genes associated with a particular differential signaling paradigm. Our NER system was used to identify MEDLINE articles differentially “expressing” NTRK1 or NTRK2 relative to each other, and then to identify other genes co-mentioned in these articles. The BTM results were then combined with microarray expression analysis results generated in an in vitro expression system where 68

either NTRK1 or NTRK2 was induced. The combined analysis provided a means to re- calculate relevance of genes that showed evidence of differential expression in both the experimental and computational systems. Finally, we experimentally validated and characterized the plausibility of predicted candidates. Materials and Methods Microarray expression profiling Full-length NTRK1 and NTRK2 were cloned into the retroviral expression vector pLNCX and transfected into Trk-null human neuroblastoma cell lines SH-SY5Y as previously described (Eggert A et al, 2000). The NTRK1 and NTRK2 over-expressing cell lines were serum-starved overnight and treated with NGF or BDNF, respectively, at 37°C for treatment times from 0 to 12 hours. Total RNA was prepared using the RNeasy Mini kit (Qiagen Inc., Valencia, CA) from NTRK1 and NTRK2-expressing cells exposed either to 100 ng/ml of NGF or 20 ng/ml of BDNF at time points 0, 1.5, 4, or 12 hrs of treatment. Microarray experiments were performed with strict adherence to the manufacturer’s instructions (Affymetrix; Santa Clara, CA). Purified biotin-labeled cRNA was fragmented, heated to 99°C for 5 min, and then hybridized at 45°C for 16 hours to HG-U133A arrays. Each data point was sampled with 3 technical and 1 biological duplicates. Expression intensity value signals corresponding to relative gene expression were calculated by the Affymetrix MAS v5.0 software package. Intensity values were then normalized (per gene) to the median of each gene’s expression across the entire experiment to account for chip-to-chip variation and to facilitate comparisons, using the RMA express software package (UC Berkeley, CA). 69

Statistical analysis of differential gene expression Normalized gene expression values were imported to the microarray data analysis toolkit Multiple Experiment Viewer (MEV) v4.0 (TIGR, Rockville, MD). Paired significance analysis of microarrays (SAM) was used to calculate differentially expressed genes between NTRK1 and NTRK2-expressing cell lines. One hundred permutations were used for multiple testing corrections during the process, and the false discovery rate was kept at zero. Text mining analysis The gene mentions of all pre-2006 MEDLINE abstracts were extracted with a previously developed named entity recognition (NER) process that uses the machine- learning technique conditional random fields to build a statistically based entity recognition model (Jin Y et al, 2006). A previously established rule-based normalization process was then applied to the extracted gene mentions, which paired human gene mentions with their corresponding official HGNC gene symbols to serve as standard referents (Fang H et al, 2006). All genes co-mentioned in a MEDLINE abstract with NTRK1 or NTRK2 were selected and co-occurrence frequencies were calculated. Genes were considered to be differentially expressed in the literature if their co-occurrence frequencies differed at least 5-fold between NTRK1 and NTRK2. Statistical pathway analysis Functional pathway analysis was performed through the Ingenuity pathway analysis toolkit (Ingenuity, Redwood City, CA). Neuroblastoma related pathways were pre-selected and the numbers of pathway-associated genes were determined for different 70

gene groups. Direct comparisons between groups were made by applying the hypergeometric statistical test in order to determine the enrichment values of neuroblastoma-relevant genes for the gene group integrating text mining results. The Bonferroni step–down correction was used to calculate the multiple-test corrected P- values for the statistical comparisons. RT-PCR validation NTRK1 and NTRK2-expressing cell lines and total RNA extractions were prepared as described above. Extracted RNAs were reverse transcribed and amplified into cDNAs using the TaqMan high-capacity archive kit (Applied Biosystems, Foster City, CA). Primers and probes for each of 11 selected genes, as well as all other assay reagents were obtained with TaqMan Gene Expression Assay kit (Applied Biosystems, Foster City, CA). The TaqMan relative quantification procedure with TaqMan 7500 instrument was applied to determine the amount of each cDNA, with the housekeeping gene GAPDH as endogenous control. Each data point had 3 technical replicates. Results and Discussion Microarray-based differential gene expression analysis In order to screen the differential responders for NGF/NTRK1 and BDNF/NTRK2 pathways, NTRK1 and NTRK2 expressing NB cell lines were made and expression profiles were obtained by microarray experiment after NGF or BDNF exposures respectively. Using the parameters specified in the Methods section, statistical analysis identified that across different time points, 751 known genes on the microarray chips were differentially expressed between NTRK1 and NTRK2-expressing cell lines after NGF or BDNF exposure. Specifically, 468 genes were found to be differentially over- 71

expressed in NTRK1 expressing cell lines relative to NTRK2-expressing cell lines, while 283 genes were observed with opposite expression behaviors (Figure 4-1). The 468 genes (gene set 1) and 283 genes (gene set 2) are listed in the attached appendix A. Integration of text mining analysis To prioritize the array-determined differentially expressed genes based on their functional relevance to NTRK1 and NTRK2 pathways, we applied pre-developed gene mention extractor and rule-based normalizer to acquire all the gene symbols co- mentioned with either NTRK1 or NTRK2. And among them, there were 514 genes preferentially associated with NTRK1 (co-occurred 5 times or more with NTRK1 than NTRK2), and 157 genes with NTRK2 (Figure 4-1). Both 514 genes (gene set 3) and 157 genes (gene set 4) are listed in the appendix A. We identified a total of 22 genes that were differentially expressed in the same manner by both the expression array and BTM methods. Of these, eighteen were differentially NTRK1 overexpressed on the chip and preferentially associated in text and four were differentially NTRK2 overexpressed on the chip and preferentially associated in text (Figure 4-1). We selected eight most overexpressed genes of the 18 NTRK1-associated genes along with three of four NTRK2-associated genes for in silico experimental validation. The reason why we chose 5 as the cut-off number was to limit the overlapping genes in order to choose manageable higher ranked genes for the following RT-PCR experiment. If we change the cut-off number to 2, the numbers of genes preferentially associated with either NTRK1 or NTRK2 are increased to 632 and 182 respectively, and the overlapping genes are increased to 31. 72

468 genes up in NTRK1, 18 genes 514 genes preferentially down in NTRK2 cell line overlapped associated with NTRK1 157 genes preferentially associated with NTRK2 283 genes up in NTRK2, 4 genes down in NTRK1 cell line overlapped Out of 10,459 known 671 genes were genes on the chips, 751 preferentially associated genes were found with either NTRK1 or differentially expressed NTRK2 in literature Figure 4-1. Differentially expressed genes on chips and preferentially associated genes in literature Functional pathway analysis In order to explore the potential relevance of the derived gene lists to neuroblastoma, we determined whether these sets were preferentially enriched for biological pathways that were known to be critical for tumorigenesis and tumor progression. The following four gene list groups were involved in this comparison: Group A: The overall gene set: all 10,459 genes represented on the expression array chip Group B: Out of Group A, the set of 751 genes differentially expressed (biologically) in neuroblastoma cell lines constitutively expressing NTRK1 or NTRK2 and induced with corresponding ligand. Group C: Out of Group A, the 550 genes that were differentially represented in the literature between NTRK1 and NTRK2 Group D: 22 genes were consistently differentially expressed, either for NTRK1 73

or NTRK2, by both techniques Functional pathways assigned to each gene in the above groups were identified with the Ingenuity pathway analysis toolkit. We concentrated on six specific pathways considered to be highly relevant to neurotrophic factor signaling in neuroblasts: cell death, cell growth and proliferation, cell-to-cell signaling and interaction, cell morphology, nervous system development and function, and cellular assembly and organization. For each functional group, the number and the proportion of genes assigned to each of those six pathways were calculated (Table 4-1). Group A Group B Group C Group D (N=10,459) (N= 751) (N= 550) (N=22) CD 1979, 18.9% 153, 20.4% 309, 56.2% 12, 54.5% CGP 2251, 21.5% 154, 20.5% 304, 55.3% 3, 13.6% CCSI 1492, 14.3% 57, 9.98% 186, 33.8% 7, 31.8% CM 1068, 10.2% 85, 11.3% 219, 39.8% 7, 31.8% NSDF 897, 8.58% 108, 19.6% 148, 26.9% 9, 40.9% CAO 755, 7.22% 103, 13.7% 115, 20.9% 11, 50% Table 4-1. The number and proportion of genes in each gene group associated with selected pathways. CD: cell death; CGP, cell growth and proliferation; CCSI, cell-to-cell signaling and interaction (CCSI); CM, cell morphology; NSDF, nervous system development and function; CAO, cellular assembly and organization. As shown in Table 4-1, when compared to the overall set of genes that were surveyed for expression levels (Group A), the subset of 751 genes identified as being significantly differentially expressed by expression array analysis alone (Group B) was slightly or moderately enriched for four pathways (CD, CM, NSDF, and CAO) and was actually reduced in the other two pathways (CGP and CCSI). Conversely, the set of genes differentially mentioned in text (Group C) was highly enriched for all six relevant pathways relative to the overall set and the expression array-alone set. Correspondingly, 74

the set of genes differentially expressed in both the microarray and text mining experiments were highly enriched for five of the six pathways. However, the CGP pathway did not show enrichment. To illustrate the Ingenuity determined genes that are relevant for select pathways, all the genes in Group C subsets are listed in Appendix B. Group B Group C Group D CD 0.152 0.0166 <0.001 CGP 0.746 0.0216 0.728 CCSI 0.999 0.0227 0.009 CM 0.146 0.0109 0.001 NSDF <0.001 <0.001 <0.001 CAO <0.001 <0.001 <0.001 Table 4-2. Significance testing for six relevant protein pathways. Shown are P-values calculated in comparisons between Groups B, C, or D relative to group A for each of the six pathways. Pathway abbreviations are listed in Table 4-1. In order to calculate statistical significance of the six selected pathway gene enrichments for the three subset groups, compared to the overall gene Group A, a hypergeometric test was applied and the corresponding P-values were calculated (Table 4-2). The results show that both the text-mining Group C (all 6 pathways) and the combined analysis Group D (5 out of 6 pathways) gene sets were enriched from the overall set for selected pathways with statistical significance. Interestingly, the expression array Group B gene set was only enriched for the NSDF and CAO pathways. To determine whether the combined analysis Group D gene subset was further enriched from the expression array Group B gene set, Group B was used as a reference set to directly determine whether Group D showed significant enrichment (Table 4-3). 75

Group D CD <0.001 CGP 0.727 CCSI 0.00940 CM 0.0124 NSDF <0.001 CAO 0.0117 Table 4-3. Significance testing for six relevant protein pathways. Shown are P-values calculated in a comparison between Group D relative to group B for each of the six pathways. Pathway abbreviations are listed in Table 4-1. The Bonferroni step-down correction was applied to account for multiple testing. Table 4-3 shows that the P-values for 5 out of 6 pathways are significant, demonstrating the relevant gene enrichment capability of the integrated analysis method compared to expression array analysis alone. This experiment suggests that at least in this experimental paradigm, our text mining process is capable of enriching gene sets for genes that are members of functional pathways critical for tumorigenic and tumor progression processes in neuroblastoma. RT-PCR Experimental Validation To determine the authenticity of genes identified by both text mining and expression analysis, we selected 11 genes for further validation of expression, using RT- PCR. Identically to the expression array experiments, gene expression levels were measured at four time points to cell lines expressing stably transfected NTRK1 or NTRK2, after applying the corresponding neurotrophic factors to the media. Generally, the RT-PCR results confirmed and more precisely defined the expression level differences observed between the NTRK1 and NTRK2 expressing cell lines by the microarray analysis. Specifically, expression level differences were concordant for 10 of 76

11 genes (Table 4-4). The gene GNAS was the lone outlier; GNAS was identified as preferentially over-expressed in NTRK2-induced cell lines relative to NTRK1-induced lines by RT-PCR, but the opposite was true both in the expression array and text mining experiments. Microarray Literature RT-PCR TBC1D8 ↑NTRK2* ↑NTRK2 ↑NTRK2 VSNL1 ↑NTRK2 ↑NTRK2 ↑NTRK2 CAMK4 ↑NTRK2 ↑NTRK2 ↑NTRK2 RPS6KA1 ↑NTRK1 ↑NTRK1 ↑NTRK1 EFNB3 ↑NTRK1 ↑NTRK1 ↑NTRK1 B3GAT1 ↑NTRK1 ↑NTRK1 ↑NTRK1 GNAS ↑NTRK1 ↑NTRK1 ↑NTRK2 NEFH ↑NTRK1 ↑NTRK1 ↑NTRK1 NEFL ↑NTRK1 ↑NTRK1 ↑NTRK1 INA ↑NTRK1 ↑NTRK1 ↑NTRK1 TYRO3 ↑NTRK1 ↑NTRK1 ↑NTRK1 Table 4-4. Differential behavior of 11 highly differentially expressed genes, as determined by three independent approaches. * The designation ↑NTRK2 indicates that the overall expression level of this gene is higher in NTRK2-expressing, BDNF-induced cell lines than in NTRK1–expressing, NGF-induced cell lines for the “Microarray” and “RT-PCR” columns. For the “Literature” column, it indicates that this gene is preferentially associated with NTRK2 to NTRK1 in biomedical text. The inverse corollary association is true for the ↑NTRK1 designation. The objective of this study was to identify immediate-to-early response genes expressed differentially between the two NTRK signaling pathways that might explain the different growth behaviors of NTRK1- and NTRK2-expressing cell lines. Thus, we characterized the RT-PCR-based expression differences more closely. One gene that exhibited a striking and rapid expression induction was EFNB3. As demonstrated in Figure 4-2, RT-PCR data shows that the expression level of EFNB3 was substantially up- regulated in NTRK1-expressing cell lines, with a two-fold increase in expression 77

observed from 0 to 4 hours after NGF application. Subsequently, by 12 hours expression had decreased to the original level. Conversely, in the NTRK2-expressing cell line, the activation of signaling by BDNF had little effect on the expression level of EFNB3 in these cells. EFNB3 2.5 2 1.5 TrkA TrkB 1 0.5 0 Figure 4-2. EFNB3 RT-PCR gene expression patterns in NTRK1 (blue) and NTRK2 (pink)- expressing cell lines. Error bars are not shown. Variation for each data point was less than ±5‰. EFNB3 (ephrin-B3) belongs to a family of ligands that bind to Eph family receptor tyrosine kinases and has been implicated in axon guidance and other patterning processes during vertebrate nervous system development (Bergemann AD et al, 1998). Remarkably previous studies have demonstrated that EFNB3 exhibits growth-suppressive activity against neuroblastoma cells in vitro. Along with NTRK1, EFNB3 has been identified as a gene whose expression is preferentially and significantly associated with low tumor stage and favorable clinical outcomes in neuroblastoma primary tumors (Tang XX et al, 1999, 2000, 2004). The RT-PCR experiment shown in Figure 2 revealed the different responses of EFNB3 expression after the activation of NTRK1 and NTRK2 signaling pathways. The up-regulation of EFNB3 mRNA in NTRK1 expressing cell line indicates that NGF/NTRK1 signaling directly or indirectly activates the expression of EFNB3, while 78

BDNF/NTRK2 signaling has no substantial effect in this time range. TYRO3 1.4 1.2 1 0.8 TrkA 0.6 TrkB 0.4 0.2 0 Figure 4-3. TYRO3 RT-PCR gene expression patterns in NTRK1 (blue) and NTRK2 (pink)- expressing cell lines. Error bars are not shown. Variation for each data point was less than ±5‰. Another gene with sizable differential expression was TYRO3. As seen in Figure 4- 3, TYRO3 expression was up-regulated by 20% in response to NGF-NTRK1 signal transduction but remained unchanged in BDNF-NTRK2 signaling from 0 to 1.5 hours after neurotrophin application. After 1.5 hours, TYRO3 expression decreased in both cell lines, but the expression level differential actually continued to increase between the two cell lines to 50% by 12 hours. TYRO3 is a trans-membrane receptor tyrosine kinase that is activated by the ligand GAS6. The exact biological function of this signaling pathway is yet to be determined. However, prior studies indicate that GAS6 promotes human fetal oligodendrocyte survival and maturation by receptor activation and downstream signaling, via the PI3-kinase/Akt pathway, in the absence of cell proliferation (Shankar SL et al, 2003). Additional evidence suggests that GAS6 may contribute to cell adhesion, immune responsiveness, and osteoclastic bone resorption through the MAPK signaling pathway (Crosier KE et al, 1997; Heiring C et al, 2004). Additionally, both light and heavy polypeptide neurofilaments (NEFL and NEFH) 79

were up-regulated in NTRK1-expressing cell lines while down-regulated in NTRK2 expressing cell line early after neurotrophin application (0 to 1.5 hr). These expression changes might be expected to lead to changes in the cytoskeleton associated with differential cellular growth and differentiation status between the two cell lines. Indeed, addition of NGF induces neurite outgrowth in many neuroblastoma cell lines, and neurite outgrowth has been shown to be positively correlated with neurofilament expression in neuroblastoma (Linnala A et al, 1998). Finally, because of time constraints, we have only done 3 technical duplicates for each data point in RT-PCR validation. However ideally, biological duplicates with independently extracted RNAs from different batches of transfected cell lines should be analyzed in order to minimize the possibility of errors. Researchers are confronted with a constant acceleration in the generation of accumulated biomedical knowledge captured both in structured, readily generated forms such as whole genome expression profiles, and from unstructured information exemplified by biomedical literature. As such, researchers are increasingly in need of novel means to capture, manage, and productively synthesize this information for specific biomedical application. Systematic data mining approaches such as the text mining tools illustrated in this study can assist with ranking tasks using previously discovered but disparate facts. This study was designed to integrate literature-based knowledge with the analysis of high-throughput array data. Our results suggest that application of an unbiased text mining-based method is capable of not only enriching for genes relevant to particular biological process, but also that this process provides a relevance ranking that may be significant for identifying plausible candidate genes involved in differential processes. The EFNB3 gene co-occurred with NTRK1 in the literature in five articles but did 80

not co-occur with NTRK2 at all. According to our hypothesis, this differential association in biomedical text can be a strong indication that EFNB3 might play a specific role in differential signaling between NTRK1 and NTRK2. In this case, the EFNB3 results can be taken as a validation of the precision of the methods employed, but it is an expected result both in terms of the literature reference and our verification of published expression correlations between NTRK1 and neuroblastoma. However, the previously published reports did not examine NTRK2 expression. Thus, our approach provided an example of literature-based discovery by generating a higher relevance ranking for EFNB3 as a differential signaling candidate than the expression array data alone indicated. More experimentation is indicated but also required to determine a potential role for EFNB3 in neuroblast differentiation. The fact that there was only 1 co-occurring paper showing the indirect association of TYRO3 with NTRK1 indicates the lack of previous investigation of TYRO3 in normal and malignant neuroblast development or neurotrophin signaling pathways. However, the possible roles of TYRO3 in cell proliferation and survival as well as its differential responses to NTRK1 signaling demonstrated by RT-PCR make further studies worthwhile. To put the text mining power into perspective, among the 1576 genes co-occurred with NTRK1 and 3882 articles describing NTRK1, it is not easy with manual effort to identify EFNB3 (5 co-occurrence papers) and even harder for TYRO3 (only 1 co-occurring paper). Since the text mining processes employed in this study are highly task-specified and perform with high accuracy, we demonstrated that even a relatively straightforward text mining application, when combined with molecular data analyses, appears to make better predictions. This process is easily scaled to lots of genes, so that many gene 81

interactions could be simultaneously surveyed for larger data sets or combinations of data sets. Thus, with little additional effort, one could use the literature to "pre-annotate" all gene probes so that they could be sorted by literature findings with ease. If additional entity classes are added, the capabilities multiply geometrically. For example, we can create an information matrix integrating genes with malignancy attribute classes. Then the gene-clinical stage relation would tell us the gene sets associated with early and late stages in addition to knowing the gene-gene associations. Co-occurrence-based information extraction can be further improved in a variety of ways such as using proximity-based measures. Generally, article-level co-occurrence can achieve high recall rates but lacks the ability to distinguish different types of relations or to adequately relevance rank such associations. For example, when we extracted all co- occurred genes with NTRK1, genes related both directly and indirectly to NTRK1 were extracted equally. As NLP-based information extraction methods continue to advance, it is likely that deeper computational understanding of the syntactic and semantic representations of text will lead to more successful and precise biomedical applications. Recent work in identifying and extracting entity relations shows promise in this regard (Jenssen TK et al, 2001; Rzhetsky A. et al, 2004). 82

Reference Bergemann AD et al: Ephrin-B3, a ligand for the receptor EphB3, expressed at the midline of the developing neural tube. Oncogene 16(4):471-80. (1998). BioCreAtIvE: Critical Assessment of Information Extraction systems in Biology. http://biocreative.sourceforge.net/index.html Borrello MG et al: TRK and TET protooncogene expression in human neuroblastoma specimens: high-frequency of TRK expression in non-advanced stages. Intl. J. Cancer. 54: 540-545. (1993). Brodeur GM: Neuroblastoma: biological insights into a clinical enigma. Nature Rev. Cancer 3: 203-216. (2003). Crosier KE et al: New insights into the control of cell growth; the role of the Axl family. Pathology 29: 131-135. (1997). Eggert A et al: Expression of the neurotrophin receptor TrkA down-regulates expression and function of angiogenic stimulators in SH-SY5Y neuroblastoma cells. Cancer Res. 62: 1802-1808. (2002). Eggert A et al: Molecular dissection of TrkA signal transduction pathways mediating 83

differentiation in human neuroblastoma cells. Oncogene 19: 2043-2051. (2000). Fang H et al: Human Gene Name Normalization Using Text Matching with Automatically Extracted Synonym Dictionaries. BioNLP (2006). Hanisch D et al: Rule-based protein and gene entity recognition. BMC Bioinformatics, 6, S14 (2005). Heiring C et al: Ligand recognition and homophilic interactions in Tyro3. J. Bio. Chem. 279(8): 6952-6958. (2004). Ho R et al: Resistance to chemotherapy mediated by TrkB in neuroblastomas. Cancer Res. 62: 6462-6466. (2002). Jessen TK et al: A literature network of human genes for high-throughput analysis of gene expression. Nature Genet. 28: 21-28. (2001). Jin Y et al: Automated recognition of malignancy mentions in biomedical literature. BMC Bioinformatics 7: 492. (2006). Kogner P et al: Coexpression of messenger RNA for TRK protooncogene and low affinity nerve growth factor receptor in neuroblastoma with favorable prognosis. Cancer Res. 53: 2044-2050. (1993). 84

Linnala A et al: Neuronal differentiation in SH-SY5Y human neuroblastoma cells induces synthesis and secretion of tenascin and upregulation of a integrin receptors. J. Neurosci. Res. 49: 53-63. (1998). McDonald RT et al: An entity tagger for recognizing acquired genomic variations in cancer literature. Bioinformatics 22(20): 3249-3251. (2004). Nakagawara A et al: Inverse relationship between trk expression and N-myc amplification in human neuroblastomas. Cancer Res. 52: 1364-1368. (1992). Nakagawara A et al: Association between high levels of expression of the Trk gene and favorable outcome in human neuroblastomas. N. Engl. J. Med. 328: 847-854. (1993). Nakagawara A et al: Expression and function of TRK-B and BDNF in human neuroblastomas. Mol. Cell. Biol. 14: 759-767. (1994). Rzhetsky A et al: GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J. Biomed. Inform. 37: 43-53. (2004). Shankar SL et al: The growth arrest-specific gene product Gas6 promotes the survival of human oligodendrocytes via a phosphatidylinositol 3-kinase-dependent pathway. J Neurosci. 23(10):4208-18. (2003). 85

Suzuki T et al: Lack of high-affinity nerve growth factor receptors in aggressive neuroblastomas. J. Natl. Cancer Inst. 85: 377-384. (1993). Tang XX et al: High level expression of EPHB6, EFNB2, and EFNB3 is associated with low tumor stage and high TrkA expression in human neuroblastomas. Clin. Cancer Res. 5: 1491-1496. (1999). Tang XX et al: Implications of EPHB6, EFNB2, and EFNB3 expressions in human neuroblastoma. PNAS 97(20): 10936-10941. (2000). Tang XX et al: Favorable neuroblastoma genes and molecular therapeutics of neuroblastoma. Clin. Cancer Res. 10: 5837-5844. (2004). 86

Chapter 5. General Conclusions and Future Directions The increasing demand for transforming unstructured biomedical research literature into a form amenable to computational analysis provides both opportunities and challenges for biomedical text mining. This dissertation started with a basic aspect of BTM research, the definition of target biomedical entities. The complexity and criticality of this endeavor has been underappreciated by the text mining community, which has largely approached this problem from a computational linguistics perspective. Through an extensive and iterative process, literature-based definitions were developed as they emerged from a consensus-building process by annotators and domain experts. In addition to the semantic challenges caused by the conceptual complexity of biomedical entities, syntactical challenges were also dealt with by establishing specific annotation guidelines in order to define distinct textual boundaries for each entity class. Using this process, entity classes for genes, RNAs, and proteins; genomic variations; types of malignancy; and phenotypic and clinical attributes of malignancy were carefully established with distinct boundaries semantically and syntactically. Training data generated through manual annotation in select corpora with those refined definitions allowed the development of automated NER extractors, based on machine learning algorithms, with accuracy rates satisfactory for specialized application by biomedical researchers. Entity mentions were then extracted from pre-2006 MEDLINE abstracts and normalized to unique identifiers through a rule-based computational procedure. Finally, this thesis focused on BTM’s discovery capabilities by integrating text mining results with high throughput data analysis to prioritize genes involved in differential cell 87

developmental signaling in neuroblastoma. Protein pathway analysis showed that the addition of literature-based information was able to effectively re-prioritize functionally relevant genes identified by microarray expression analysis. Experimental validation of these results demonstrated that these re-prioritized genes were verifiable candidates worthy of additional experimental characterization. This text mining integrated method provides researchers a systematic and objective way to analyze the experimental data and better hypothesize targets for the next step research based upon previously discovered and published knowledge. With the steadily accelerating pace of biotechnological development and knowledge accumulation, there is an increasing need of having well-performed BTM systems available for a variety of purposes, including information extraction, document retrieval and literature-based discovery. As end users struggling to manage and synthesize an overwhelming amount of research information, it is prudent for biologists to closely collaborate with computer scientists on every front, including the adaptation of BTM research to assist with solving biomedical problems. This dissertation has focused upon investigations that attempt to build BTM systems with more biological input that is infused throughout the process. Accordingly, as an essential building block of many BTM tasks, the development of our named entity recognition system incorporated biological perspectives, which has been instrumental for the success of biomedical applications built upon this process, such as our successful gene-centric information retrieval system FABLE (FABLE). The performance of entity extractors developed by our approach depends heavily on the quality and quantity of training data. We have spent substantial amount of time 88

creating manually annotated corpora in order to develop high-performance extractors. However, further research should be conducted on deciding the scope and size of the training data to make the process most cost effective. Normalization algorithms that incorporate disambiguation schemes are also desired for improving entity recognition performance since it is difficult for a pure rule-based approach to solve the problem of ambiguous matches between mentions and unique identifiers. Effective disambiguation approaches would likely need to survey distant contextual information in order to determine the correct match (Chen L et al, 2005). Deeper parsing of the entity relations is another natural extension of this thesis research. With the incorporation of linguistic analysis that includes deeper syntactic and semantic processing (such as the parse tree and semantic role labeling systems developed at Penn), entity relationships could be further mined with more precision and granularity. For example, extraction of specific causal relationships between genes and malignancy types from biomedical literature would be an important advance in application. Along with the maturation of the mentioned BTM tasks, it will be possible to construct a structured and queryable cancer knowledgebase integrating the most complete and up-to-date genomic, phenotypic and clinical information from the published biomedical records, based on which, further interpretation of the experimental data will lead to more reliable and frequent literature-based discovery and hypothesis generation. 89

Appendices A. Genes that differentially expressed on the array chips (gene sets 1 and 2) and preferentially associated in the literature (gene sets 3 and 4) Gene Set 1 Gene Set 2 Gene Set 3 Gene Set 4 ABCA3 AASS ABCA4 AA ABCG1 ABLIM1 ABCB6 ABCA3 ABHD11 ABT1 ABCC1 ACVR1B ABLIM3 ACCN2 ABL1 ACVR2A ACHE ADCY9 ACACA ADD1 ACOT7 AFF4 ACCN3 ADORA1 ACTL6B AGA ADAM17 ADRA1B ACTN1 ALDH18A1 ADCY1 APLP1 ADAM12 AMMECR1 ADRA2B ATN1 ADAM23 ANGPT1 ADRM1 ATXN3 AEBP1 ANGPTL2 AGTRL1 BHLHB4 AES ANTXR1 AHSA1 BMPR2 AGPAT7 AQP3 AK1 BRD8 AGRIN ARL5A AKT1 C1QL1 ALK ASCC3 ALB CA2 ALMS1 ATP2A2 ALDH3A2 CAMK4 AMT ATP5F1 ALPK2 CBL ANK1 ATP5G3 AMIGO2 CCND2 AP3M2 BAG1 ANPEP CD160 APBA2BP BAK1 ANXA2 CD63 APC2 BAZ1A ANXA5 CDCA5 APEH BCL11A AP3B1 CDK5R1 APLP1 BDH2 APAF1 CDKN1C ASF1B BID APC CETN1 ASMTL BTN2A1 APOE CMD1B ASPHD1 BTN2A2 AQP1 CORT ASRGL1 BZW2 AR CREM ATP13A2 C12orf11 ARHGAP24 CRH ATP1A3 C12orf5 ARHGAP5 CRP ATP8B3 C14orf156 ARHGEF7 CTF1 B3GAT1 C14orf166 ARTN CYP19A1 B3GAT3 C20orf121 ASCL1 DBH B4GALNT1 C21orf91 ASGR2 DCN B4GALT5 C2orf25 ATF2 DDIT3 BAHCC1 C4orf9 ATF7IP DEFA1 BAI1 C5orf13 ATP7A DLG3 BAI2 C6orf120 AVP DLG4 90

BAZ2A C8orf41 AXL E2F1 BCORL1 C9orf82 B3GAT1 EFNA5 BDH1 CALD1 B4GALT1 EGR2 BEX1 CAMK4 BAD EMX2 BRSK2 CBFA2T2 BBS2 EPHA3 BSN CCNC BCAR1 EPHA4 C14orf79 CCNJ BCL2 ERG C18orf10 CD164 BCR ERP29 C1orf159 CD99 BDKRB1 ETV1 C1orf164 CDK6 BMP2 EXOSC1 C1orf21 CHGB BMP6 F2R C1orf66 CHM BRAF F3 C1QL1 CHRNA3 BSG FMR1 C20orf103 CLIC4 C15orf15 FSHR C20orf12 CNBP C18orf10 GAD1 C20orf149 CNN3 C21orf33 GCLC C20orf195 COL4A1 C2orf28 GFPT1 C20orf20 COPS2 C2orf3 GLTSCR2 C20orf46 CPSF6 C3 GNAI3 C22orf9 CRABP2 C7 GPSM2 C2orf17 CSRP2 CAD GRIA4 C2orf24 CTNNA1 CAMK1 GRIN2A C3orf18 CTR9 CAPN2 GRIN2B C3orf32 CTSC CASP2 GRIN3A C6orf134 CYFIP1 CASP8 GRK1 C7 DAP CASP9 GRM1 C7orf43 DDEF1 CAV1 GRM6 CACNA1B DDIT4 CAV3 GSR CACNB1 DDOST CCDC6 GUK1 CACNG2 DDX17 CCL14 HDAC2 CACNG4 DDX3X CCL2 HFE CALML4 DECR1 CCL4 HMGB2 CAMK2N1 DIMT1L CCL5 HRB CAMKV DKK1 CCND1 HSF1 CAMTA1 DLL3 CCND3 HSPA4L CAPN5 DMD CCRK HSPA8 CARD10 DNAJB6 CCT4 HSPH1 CBR3 DPYSL3 CD1A HTR1A CCBL1 DUSP1 CD38 ID2 CCDC92 DUSP22 CD40 IER5 CCNA1 ECHDC1 CD40LG IL6ST CCNE1 EEF1A1 CD44 IRAK2 CD81 EFNA4 CD68 ITM2C CDC2L6 EFNB2 CD79A ITPR1 CDC42 EIF2S1 CD80 ITPR2 CDC42BPB EIF2S3 CD86 KCNJ3 CDKN2D EIF3S1 CDC2 KCNJ5 CDO1 EIF3S6 CDC25C KCNJ6 91

CELSR2 EIF5 CDH1 KIF3A CENPM ELAVL1 CDK4 LATS1 CENTD3 ELK3 CDKN1B LMO4 CHD5 ETF1 CEACAM4 MAOA CKAP1 ETFB CEBPZ MAP2K2 CLCN6 ETFDH CEL MAPRE1 CLSTN2 FAM111A CENPJ MC4R CNNM1 FAM3C CHKA MPL CNTNAP2 FAM98A CHL1 MPO COL8A2 FAT CHN1 MYH6 CPT1A FKBP14 CHRM2 MYO1A CRABP1 FKBP1A CHUK NBN CRMP1 FLNC CILD2 NCK2 CRTAC1 FLRT1 CNP NEUROD1 CSPG3 FLRT3 CNR1 NFATC4 CSPG5 FN1 CNR2 NNAT CTBS FSTL1 COL11A2 NP CTNNA2 FYCO1 CP NPAT CXCR4 FZD2 CRK NR1D1 CYLN2 GALNT10 CRKL NR3C2 CYP1B1 GATA4 CRS NSF DAAM1 GGCX CSF1R NTRK1 DDX25 GHITM CSF2 NYX DENND2A GLUL CSF2RA ODC1 DEPDC5 GNL3 CSF3 OTC DGKD GPM6B CSK OTX1 DHPS GPR125 CTBS PABPC1 DLG4 GSPT1 CTNNA1 PABPN1 DNAJB5 H2AFY CTSB PAX3 DNM1 HEATR1 CXCL1 PDIA4 DPP6 HEBP2 CXCL12 PHOX2A DPYD HEMK2 CYLN2 POMC DRAP1 HERPUD1 DCC POU3F1 DRD2 HOMER3 DDR1 PPARA DSTN HOXC10 DDX41 PPM1L DUSP8 HSP90B1 DECR1 PSD DUT IGF1R DHDDS PSEN1 DYNLT3 IL13RA1 DLX2 PSEN2 EDG4 IPO7 DNAH8 PSMD8 EDG7 ISL1 DNAJA2 RAB40B EFHD2 ITPA DNM2 RABEP2 EFNB3 JAM2 DOK1 RARB EGFL7 JMJD1C DR1 RGS4 EGFL9 JMJD2C DRG2 RTCD1 ELAVL4 KCNJ8 DUSP1 SD EML2 KDELC1 ECEL1 SHANK2 ENO2 KIAA0020 EDG1 SI EPB41L1 KIAA0247 EDG2 SLC12A5 92

EPB41L4B KLHL20 EDG5 SLC1A3 EPB49 LANCL2 EDN3 SLC30A7 EPOR LGR5 EEF1A1 SLC6A1 EPS8L1 LZTS1 EEF1A2 SLC6A3 ETNK2 MAGEA1 EFNA2 SLC6A4 F12 MAGEA5 EFNB2 SRI FAAH MAGOH EFNB3 ST3GAL6 FAM105A MAN1A2 EFS SULT FAM65A MAX EGF SYN1 FBXO2 MBD2 EGFR SYT4 FEZ1 MCCC2 EIF2C2 TBC1D8 FEZ2 MEIS1 ELA2 TERT FGD1 METAP2 ELAVL3 TIMP2 FKBP1B METT10D ELK3 TPH1 FKBP4 MFAP4 ENPP1 TRPC3 FLII MINA EPHA1 TSC1 FLOT1 MNAT1 EPHB1 TSC2 FNBP1 MOBK1B EPHB6 TWIST1 FNDC4 MPHOSPH10 EPO TXNRD2 FOXRED2 MPZL1 ERBB2 VAMP2 FRS3 MRPL42 ERBB3 VCAM1 FRY MRPL44 ERBB4 VSNL1 FUT1 MTMR1 EREG WARS G6PC3 MTO1 ERVK5 XYLT1 GABRB3 MYO1E ESR2 GALE MYO5C ETV5 GAP43 MYO6 ETV6 GAS2L1 NAT1 EVI1 GDAP1L1 NBN EWSR1 GDF1 NCOA4 F2 GDI1 NDUFA4 F7 GFRA3 NEBL FANCB GNAO1 NECAP2 FBN1 GNAS NEDD4 FBS1 GNAZ NOC3L FCER1A GNB5 NOLA1 FDFT1 GNG3 NOTCH2 FDPS GPR153 NUDT4 FES GPR19 PDE4B FGF3 GPRASP1 PDGFRL FGF4 GRIK5 PDIA3 FGFR4 GRIN1 PDLIM3 FH GRK6 PELI1 FKBP1A GTPBP2 PEX3 FKBP4 GTSE1 PHACTR2 FKBPL GUCA1A PHLDA1 FLT1 H2AFX PIK3R3 FLT4 HDAC6 PKP2 FN1 93

HECTD3 PLA2G12A FOLH1 HMBS PLAGL1 FOSL1 HMG20B PLEKHC1 FOXM1 HPCAL4 PLS3 FOXO1A HPS6 PLSCR1 FOXO3A HRASLS3 PNRC2 FRS2 HRH3 PON2 FSCN1 HTR1E POPDC2 FUS HTRA2 PPA1 FUT3 HUWE1 PPP2R1B FZD3 HYI PPP4R2 GAB1 IBRDC3 PRKD3 GABRA1 ICAM2 PTPRD GAK IFT122 PTTG1IP GAS6 IGSF4 QKI GCK IL27RA RAB27A GEM INA RABEP1 GGH IQCK RAMP1 GGT1 IQSEC1 RAP2C GH1 IQSEC3 RB1 GH2 ITSN1 RBM3 GIF JAG2 RBPMS GIPC2 JPH3 RBPSUH GIPC3 JUND RCN1 GJA1 KCNA3 RETSAT GJA8 KCNB1 RIT1 GNAS KCNC1 RND3 GOLGA5 KCNH6 RNF13 GPI KCNK12 RNF130 GPR88 KCNK3 RPL23 GPT KCNQ1 RPS21 GRHL3 KCNQ2 RPS9 GRIA1 KIAA0649 RSL1D1 GRIK1 KIAA1539 RSU1 GRLF1 KIF13B RWDD1 GSTP1 KIF1A RYBP GSTZ1 KIF3C RYK GTF2B KIF5C SCLY GTF3A KLF11 SCYE1 HCCS KNS2 SDC4 HCK L1CAM SERBP1 HD LAGE3 SERPINF1 HDAC1 LIG1 SERTAD2 HES1 LIN7B SF3B1 HGF LPHN1 SH3BGRL HK2 LRP8 SHOX2 HLA-E LRRFIP2 SKP2 HM13 LRRN5 SLC31A2 HPSE 94

LSM14B SLC33A1 HRAS LY6E SLC39A14 HSN2 MADD SLC39A8 IARS MAGED1 SMAD5 ICAM1 MAP7 SMARCC1 IER3 MAPK11 SNAP23 IFI44 MAPK12 SNX13 IFNA1 MAPK8IP2 SP110 IFNA17 MAPT SSBP1 IFNB1 MARK4 STAT5B IKBKB MAST1 STEAP1 IL13RA2 ME3 SUCLG2 IL17F MLH3 SYNCRIP IL1A MMP15 SYPL1 IL2 MMP24 TBC1D8 IL3 MPP2 TCEB3 IL4R MPP3 TCF7L1 IL6 MRPL2 TCF7L2 INA MSH6 TES IRAK1 MSN TFB2M IRAK3 MTMR2 TFDP2 IRF1 MTSS1 TGFBR2 IRS1 MYD88 TGIF ISL1 MYO1D TGIF2 ITGB1 MYOZ3 TH1L IV MYT1L TJAP1 JAK2 NAGA TLE4 JAK3 NCAM1 TMCO1 JUNB NCOA6 TMEM109 KCND2 NDE1 TMEM33 KDR NEDD4L TMEM39A KIT NEFH TMEM43 KITLG NEFL TOMM20 KLF7 NELL1 TOP1 KLK3 NFASC TOR1AIP1 KNG1 NLGN4X TP53 KRAS NMNAT2 TRAM1 LARGE NMU TRIM5 LAT NOS1AP TRMU LBX1 NPDC1 TROVE2 LCS1 NRCAM TSPAN12 LGALS1 NRGN TSPAN13 LGALS3 NRXN1 TSPAN6 LGI1 NRXN2 TXNDC1 LOX NTRK1 VIM LRP1 NUDCD3 VPS54 LRPAP1 NUP210 VSNL1 LRRC21 OAS3 WDR73 LTA 95

OBSL1 WDR77 LTF ODF2 YIPF6 MAG OGDHL ZFAND5 MAGED1 OGG1 ZMPSTE24 MAGED2 OLFM1 ZNF238 MAK OSBPL2 ZZZ3 MAP2K1 OXCT1 MAP3K1 PACRG MAP3K11 PAFAH1B3 MAPK10 PAK3 MAPK8 PAK4 MAPK9 PAOX MARCKS PAQR4 MAS1 PARD6A MBTPS1 PARP6 MDK PARVA MET PAX5 MGAT3 PCTK1 MIA PCYT2 MIB1 PDE2A MICA PDE9A MKI67 PDLIM7 MKKS PER3 MLLT7 PEX14 MME PFKL MMP2 PFKP MMP3 PGBD5 MMP9 PGLS MNG1 PHF1 MOS PHTF1 MRGPRF PIK3CD MSN PIM1 MUSK PITPNM1 MYB PIWIL1 MYC PKN1 MYLK PKP4 MYO1E PLXNA2 MYOD1 PNKP NANS PNMA2 NBL1 PNMT NCOA1 PNPLA4 NCOA4 PORCN NDN PPAP2C NEDD9 PPEF1 NEFH PPM1G NEFL PRKD2 NEU1 PRNP NF1 PRPSAP1 NFKB1 96

PSD NFKBIA PTGER3 NFKBIB PTOV1 NFKBIL1 PTPRN NGFR PTPRN2 NMB PXMP2 NME1 R3HDM2 NOLC1 RAB15 NPC1 RAB3A NPY1R RAB3B NR1I2 RAB6B NRGN RABAC1 NRK RAD23A NT5E RAD51L3 NTRK2 RAGE NUMB RAI2 OCM RALY OED RAMP2 OSM RAP1GAP P2RX1 RASGRP2 P2RX3 RGL2 PAF1 RGS11 PAH RIMS2 PCNA RIT2 PDGFB RND2 PDGFRL ROGDI PDIA3 RPH3A PDK1 RPP25 PFN1 RPRC1 PGM2 RPS6KA1 PGR RTN1 PHB RTN2 PHB2 RUFY3 PIM1 RUSC1 PITX2 RUSC2 PKD1 SAC3D1 PKLR SAMD14 PLA2G1B SAP130 PLAU SCAMP5 PLEK SCG5 PLG SCN2A2 PLXNB1 SCN3A PLXNB2 SCN3B PNN SCRN1 PPARG SDC2 PPBP SEC61A2 PPP1R13L 5-Sep PPP1R1B SERPINA5 PRKCA 97

SETD3 PRKCB1 SEZ6L PRKCE SH2B3 PRKCZ SHANK2 PRKD1 SHB PRKG1 SHC2 PRL SIX3 PRLR SIX6 PROZ SLC18A3 PRPH SLC22A17 PRRXL1 SLC25A1 PSAP SLC2A4RG PSMA5 SLC43A3 PSMB6 SLC4A3 PSPN SLC8A2 PTBP1 SMARCC2 PTCH2 SMARCD3 PTEN SMPD3 PTGDR SMTN PTGER1 SNAP25 PTGFR SNAP91 PTH SNAPC2 PTK2 SNCB PTN SNX27 PTPN1 SOD1 PTPN13 SORBS1 PTPN6 SOX13 PTPRB SPA17 PTPRC SPAG4 PTPRF SPAG6 PTPRO SPRY2 PTX3 SPTAN1 PXN SRD5A1 PYCARD SRPK2 PZP STMN2 RAB7 STMN4 RAC1 STUB1 RAF1 STX1A RAP1A STX2 RAPGEF1 STXBP1 RAPGEF5 STXBP5L RASA1 SULT4A1 RASSF1 SYN1 RB1 SYNGR3 RBL1 SYP RDX SYT17 REL SYT5 RELA TAZ RGS19 98

TBX3 RHOA TCEA2 RHOT2 TCEAL2 RIPK2 TCF25 RNGTT TEAD4 ROCK1 TFR2 ROR1 THRA ROR2 THY1 RP21 TLE2 RPE TM2D3 RPS6KA1 TMCC1 RPS6KB1 TMEM121 RUNX1 TMEM153 RUNX2 TMEM22 S11 TMEM24 SCN10A TMEM28 SCN11A TMOD1 SCN9A TNIK SELE TNNI3 SELL TREX1 SEMA3F TRIM62 SEMA4D TRIP10 SEMA5A TTC9 SGK TUBB2B SHB TUBB2C SIT1 TUBB4 SLC22A4 TULP4 SLC6A2 TYRO3 SLC7A1 UBB SLCO6A1 UBE2C SMPD1 UIMC1 SMPD2 UNC119 SNRPG UNC13A SOAT1 USP4 SORT1 VAMP1 SP1 VAMP2 SPAG1 VAT1 SPHK1 VEGFB SPR WBP2 SPTLC1 WDR62 SRA1 YBX2 STAT1 YPEL1 STAT3 YWHAB STAT5A ZBTB22 STAT5B ZMAT4 STATH ZNF274 STS ZUBR1 SYCP3 T 99

TACR1 TBP TBXA2R TEK TFAP2A TFDP3 TFG TFPT TFRC TG TGFA TGFB2 TIE1 TKTL1 TLX1 TLX3 TMEM37 TNC TNFRSF10C TNFRSF25 TNFSF12 TNS1 TP53 TPBG TPM1 TPM3 TPO TPR TPSAB1 TRAF6 TRI TRIM33 TRK1 TRPV1 TSHR TTN TYMS TYR TYRO3 UGCG VAV1 VWF WT1 YES1 ZBTB25 100

B. Ingenuity determined pathway relevant genes for Group C (preferentially associated genes) CD CGP CCSI CM NSDF CAO ABCC1 ABL1 ABL1 ABL1 ABL1 ABL1 ABL1 ACACA ADAM17 ADAM17 ADAM17 ADAM17 ACACA ACVR1B AKT1 ADRA1B ADORA1 ADRA1B ACVR1B ADRA1B ALB AKT1 AKT1 AKT1 ADORA1 AKT1 AMIGO2 ALB ALDH3A2 APC AKT1 ALB ANXA2 ANPEP AMIGO2 APOE ALB ANXA2 ANXA5 ANXA2 APAF1 ARHGAP24 AMIGO2 APC AP3B1 ANXA5 APLP1 ARHGEF7 ANPEP APOE APC APC APOE ARTN APAF1 AR APOE APOE ARTN AVP APC ARHGAP5 AR AR ASCL1 AXL APOE ARHGAP24 ARHGAP5 ARHGAP5 ATN1 BCL2 AR ARTN AVP ARHGEF7 ATXN3 BMP2 ARTN ASCL1 AXL AVP BCL2 CAMK4 ATF2 ATF2 B4GALT1 AXL BMP2 CAV1 ATN1 AVP BCL2 B3GAT1 BRAF CD44 ATXN3 AXL BDKRB1 BCL2 CAMK4 CDC2 AXL BCL2 BMP2 BMP2 CDC2 CDH1 BAD BDKRB1 BSG BRAF CDK5R1 CDK5R1 BCL2 BMP2 CASP8 CAPN2 CDKN1B CDKN1B BMP2 BMP6 CAV1 CASP9 CDKN1C CENPJ BMP6 BMPR2 CBL CAV1 CHL1 CHL1 BRAF BRAF CCL2 CAV3 CHRM2 CHN1 BSG BSG CCL5 CBL CNR1 CNP C7 CAPN2 CCND1 CCDC6 CRH CNR1 CAMK4 CASP2 CCND2 CCL2 CRK CRK CASP2 CASP8 CCND3 CCL5 CASP2 CRKL CASP8 CASP9 CD38 CCND1 CASP9 CSF2 CASP9 CAV1 CD40 CCND2 CCND2 CXCL12 CAV1 CAV3 CD44 CCND3 CSF3 DCC CBL CBL CD63 CD40 CTF1 E2F1 CCDC6 CCDC6 CD86 CD44 CTNNA1 EDG1 CCL5 CCL2 CD1A CD40LG CXCL12 EDG2 CCND1 CCL5 CD40LG CDC2 DCC EDG5 CCND2 CCND1 CDH1 CDH1 DLG4 EFNB2 CCND3 CCND2 CDK5R1 CDK5R1 DLX2 EFNB3 CCRK CCND3 CHL1 CDK4 E2F1 EGF CD38 CCRK CNR1 CDKN1B EDG1 EGFR CD40 CD38 CRH CDKN1C EDG2 EPHA4 CD44 CD40 CRKL CHL1 EFNA5 ERBB2 101

CD86 CD44 CRP CHN1 EFNB2 ERBB3 CD160 CD63 CSF2 CNR1 EFNB3 ERBB4 CD40LG CD86 CSF3 CREM EGF F2 CDC2 CD160 CSF1R CRH EGFR F2R CDC25C CD40LG CSK CRK EGR2 F7 CDH1 CDC2 CTNNA1 CRKL ELAVL3 FGF3 CDK4 CDC25C CXCL12 CSF1R EMX2 FGF4 CDK5R1 CDH1 DCC CSF2 ERBB2 FGFR4 CDKN1B CDK4 DCN CSF3 ERBB3 FKBP4 CDKN1C CDK5R1 DDR1 CSF2RA ERBB4 FN1 CHKA CDKN1B E2F1 CSK ESR2 FSCN1 CHL1 CDKN1C EDG1 CTNNA1 F2 GAB1 CNP CHKA EDN3 CXCL12 FGF3 GJA1 CNR1 CNP EFNA2 CYP19A1 FGF4 HCK CNR2 CREM EFNA5 DCC FGFR4 HD CREM CRH EFNB2 DCN FKBP4 HGF CRH CRK EGF E2F1 FMR1 HRAS CRK CRP EGFR EDG2 FN1 ICAM1 CRP CSF2 EGR2 EDG5 FOXO1A IL1A CSF2 CSF3 ELA2 EFNA5 GAB1 IL2 CSF3 CSF1R EPHA3 EFNB2 GAD1 IL6 CSF1R CSF2RA EPO EFNB3 GJA1 INA CSF2RA CSK ERBB2 EGF GRIK1 ITGB1 CSK CTF1 ERBB3 EGFR GRLF1 KITLG CTF1 CTNNA1 ERBB4 EPHA4 GSTP1 KNG1 CTNNA1 CTSB F2 EPO HD KRAS CTSB CXCL12 F3 ERBB2 HES1 LATS1 CXCL12 CYP19A1 F7 ERBB3 HGF MAG CYP19A1 DBH F2R ERBB4 HMGB2 MAOA DBH DCC FES ESR2 HRAS MAP3K1 DCC DCN FGF4 ETV6 IL3 MAPK8 DCN DDIT3 FKBP1A EVI1 IL6 MAPRE1 DDIT3 DDR1 FLT1 EWSR1 IL1A MARCKS DDR1 DNAJA2 FN1 F2 ITGB1 MET DDX41 DUSP1 FUT3 F7 KITLG MMP2 DLX2 E2F1 GNAS F2R KLF7 MSN DUSP1 EDG1 HCK FBN1 KNG1 NDN E2F1 EDG2 HD FES KRAS NEFH ECEL1 EDG5 HES1 FGF3 LBX1 NEFL EDG1 EDN3 HGF FGF4 LGI1 NFATC4 EDG2 EFNB2 HPSE FGFR4 LRPAP1 NGFR EDG5 EFNB3 HRAS FKBP4 LTA NTRK1 EEF1A1 EFS HTR1A FLT1 MAG NTRK2 EEF1A2 EGF ICAM1 FMR1 MAOA P2RX1 EFNB2 EGFR IFNB1 FN1 MAP2K1 PDIA3 EGF EGR2 IKBKB FOSL1 MAPK8 PFN1 EGFR ELA2 IL2 FOXM1 MAPK9 PLAU EGR2 EMX2 IL3 FOXO1A MAPK10 PLXNB1 102

ELA2 EPHB6 IL6 FOXO3A MARCKS PLXNB2 EPHB6 EPO IL1A FZD3 MDK PRKCE EPO ERBB2 IL4R GAB1 MET PRPH ERBB2 ERBB3 IRS1 GEM MMP2 PSAP ERBB3 ERBB4 ITGB1 GJA1 MOS PSD ERBB4 EREG KITLG GPI MSN PTK2 ERG ERG KLK3 GRLF1 MUSK PTPRF ESR2 ESR2 KNG1 GRM1 NBN RAC1 ETV6 ETV6 LGALS1 HCK NDN RASSF1 EVI1 EVI1 LGALS3 HD NEFL RDX EWSR1 EWSR1 LOX HES1 NEUROD1 RHOA F2 F2 LRPAP1 HGF NFATC4 SELE F3 F2R LTA HRAS NFKBIA SEMA3F F7 FBN1 MAG ICAM1 NGFR SEMA4D F2R FBRS MAP2K1 ID2 NPC1 SEMA5A FBN1 FES MAP3K1 IKBKB NR1D1 STAT3 FCER1A FGF3 MAPK8 IL2 NRGN SYN1 FDFT1 FGF4 MAS1 IL3 NTRK1 TNC FGF3 FGFR4 MDK IL6 NTRK2 TNS1 FGF4 FKBP1A MET IL1A NUMB TP53 FKBP1A FKBP4 MME IL4R PAX3 TPM1 FLT1 FLT1 MMP2 IRAK1 PDIA3 TPR FN1 FN1 MMP9 IRF1 POMC TTN FOSL1 FOSL1 MPO IRS1 PRKG1 VCAM1 FOXM1 FOXM1 MSN ITGB1 PRPH VWF FOXO1A FOXO1A MYB JUNB PSAP WT1 FOXO3A FOXO3A NEDD9 KDR PSEN1 FSHR FSCN1 NEUROD1 KITLG PSPN FUS FUS NFKB1 KNG1 PTK2 FZD3 FZD3 NFKBIB KRAS PTN GAB1 GAB1 NTRK1 LGALS1 PTPRF GCLC GAD1 NUMB LGALS3 RAC1 GFPT1 GAK PDGFB LOX RAF1 GJA1 GEM PDIA3 LRPAP1 RB1 GNAS GH2 PGR MAG RBL1 GPI GJA1 PHB MAP2K1 RDX GRIN2A GPI PITX2 MAP2K2 REL GRM1 GSTP1 PKD1 MAP3K1 RELA GSR GTF2B PLAU MAP3K11 RHOA GSTP1 HCK PLG MAPK8 RUNX1 GSTZ1 HDAC1 PLXNB1 MAPK9 SEMA3F HCK HDAC2 PNN MAPK10 SEMA5A HD HES1 POMC MARCKS SLC1A3 HDAC1 HGF PPARG MAS1 SMPD1 HDAC2 HRAS PPBP MET SPHK1 HES1 HTR1A PRKCE MGAT3 STAT3 HFE ICAM1 PRKG1 MLLT7 SYN1 HGF ID2 PRL MMP2 TFAP2A 103

HK2 IER3 PRLR MMP9 TGFA HRAS IFNB1 PSEN1 MOS TGFB2 HSPA8 IKBKB PSEN2 MPL TIMP2 ICAM1 IL2 PTGER1 MSN TLX1 ID2 IL3 PTH MYB TLX3 IER3 IL6 PTK2 MYO1A TNC IFNB1 IL13RA2 PTN MYOD1 TP53 IKBKB IL1A PTPN6 NBN TRPV1 IL2 IL4R PTPRB NDN TWIST1 IL3 IRF1 PTPRC NEDD9 VCAM1 IL6 IRS1 PTPRF NEUROD1 WT1 IL1A ISL1 RAC1 NFATC4 IL4R ITGB1 RAF1 NFKB1 IRAK1 JUNB RAPGEF1 NFKBIB IRF1 KDR RASSF1 NGFR IRS1 KIF3A REL NPC1 ISL1 KITLG RELA NTRK1 ITGB1 KLK3 RHOA NTRK2 ITPR1 KNG1 RPS6KB1 NUMB ITPR2 KRAS SELE ODC1 JUNB LATS1 SELL PAX3 KCNJ6 LBX1 SEMA4D PDGFB KDR LGALS1 SEMA5A PDIA3 KIF3A LGALS3 SIT1 PFN1 KITLG LGI1 SLC6A2 PGR KLF7 LOX SLC6A3 PIM1 KNG1 LRPAP1 SLC6A4 PITX2 KRAS LTA STAT1 PLAU LATS1 MAG STAT3 PPARG LGALS1 MAGED1 STAT5A PPP1R13L LGALS3 MAGED2 STAT5B PRKCE LRPAP1 MAP2K1 STATH PRKCZ LTA MAP2K2 TEK PRKG1 MAGED1 MAP3K1 TERT PRL MAOA MAP3K11 TFRC PRPH MAP2K1 MAPK8 TGFA PSAP MAP2K2 MAPK9 TGFB2 PTGFR MAP3K1 MAPK10 TIMP2 PTK2 MAP3K11 MAPRE1 TNC PTN MAPK8 MAS1 TP53 PTPN6 MAPK9 MDK TPH1 PTPRF MAPK10 MET TRAF6 RAC1 MDK MGAT3 TSC1 RAF1 MET MICA TSC2 RAPGEF1 MGAT3 MLLT7 TWIST1 RARB MLLT7 MMP2 TYR RASSF1 MME MMP3 TYRO3 RB1 MMP2 MMP9 VCAM1 RBL1 104

MMP3 MPL VSNL1 RDX MMP9 MPO VWF REL MPL MYB RELA MPO MYH6 RGS4 MSN MYOD1 RHOA MUSK NBN RPS6KA1 MYB NCOA1 RPS6KB1 MYLK NCOA4 RUNX1 MYOD1 NDN SELL NBN NEU1 SEMA3F NCOA1 NEUROD1 SEMA4D NCOA4 NFATC4 SEMA5A NDN NFKB1 SMPD1 NEDD9 NFKBIA STAT3 NEFL NFKBIB TACR1 NEUROD1 NGFR TBP NFATC4 NP TBXA2R NFKB1 NPY1R TEK NFKBIA NR1D1 TERT NFKBIB NT5E TFG NGFR NTRK1 TGFA NP NTRK2 TGFB2 NPC1 NUMB TNC NR1D1 ODC1 TNS1 NRGN PABPN1 TP53 NTRK1 PAX3 TPM1 NTRK2 PCNA TPR NUMB PDGFB TSC1 ODC1 PDIA3 TSC2 PAX3 PFN1 TWIST1 PCNA PGR TYRO3 PDGFB PHB VCAM1 PDIA3 PIM1 VWF PGR PITX2 YES1 PHB PKD1 PHOX2A PLA2G1B PIM1 PLAU PKD1 PLG PLAU PNN PLG POMC POMC PPARA POU3F1 PPARG PPARA PPBP PPARG PRKCB1 PPP1R13L PRKCE PRKCB1 PRKCZ PRKCE PRKD1 PRKCZ PRKG1 105

PRKD1 PRL PRL PRLR PRLR PSAP PRPH PSEN1 PSAP PTGER1 PSEN1 PTH PSEN2 PTK2 PTGER1 PTN PTH PTPN6 PTK2 PTPN13 PTN PTPRC PTPN6 PTPRF PTPN13 PTPRO PTPRC PTX3 PTPRF RAC1 PTPRO RAF1 PYCARD RAPGEF1 RAC1 RARB RAF1 RASSF1 RAPGEF1 RB1 RARB RBL1 RASSF1 REL RB1 RELA RBL1 RGS4 RDX RHOA REL RIPK2 RELA RPS6KA1 RGS4 RPS6KB1 RHOA RUNX1 RHOT2 RUNX2 RIPK2 SELE ROR2 SEMA3F RPS6KA1 SEMA4D RPS6KB1 SGK RUNX1 SHB RUNX2 SLC1A3 SEMA3F SLC6A4 SGK SMPD2 SLC1A3 SP1 SLC6A3 SPHK1 SMPD1 STAT1 SMPD2 STAT3 SOAT1 STAT5A SP1 STAT5B SPHK1 TBC1D8 STAT1 TBP STAT3 TEK STAT5A TERT 106

STAT5B TFAP2A TACR1 TFG TBP TFRC TERT TG TFAP2A TGFA TFPT TGFB2 TFRC TIMP2 TGFA TLX1 TGFB2 TLX3 TIE1 TNC TIMP2 TP53 TNC TPM1 TNFRSF10C TPO TP53 TPR TPM1 TRAF6 TRAF6 TSC1 TSC1 TSC2 TSC2 TSHR TWIST1 TYMS TYMS TYR TYR TYRO3 TYRO3 UGCG UGCG WARS VAMP2 WT1 VCAM1 WT1 YES1 107

Bibliography Alako BT, Veldhoven A, Baal S, Jelier R, Verhoeven S, Rullmann T, Polman J, Jenster G: CoPub Mapper: mining MEDLINE based on search term co-publication. BMC Bioinformatics. 6:51. (2005). BioCreAtIvE: Critical Assessment of Information Extraction systems in Biology. http://biocreative.sourceforge.net/index.html Cairns J: The interface between molecular biology and cancer research. Mutat Res, 462: 423-428. (2000). Chang JT, Schutze H, Altman RB: GAPSCORE: finding gene and protein names one word at a time. Bioinformatics, 20: 216-225 (2004). Chen L, Friedman C: Extracting phenotypic information from the literature via natural language processing. Medinfo, 11(Pt 2):758-762. (2004). Chen L, Liu H, Friedman C: Gene name ambiguity of eukaryotic nomenclatures. Bioinformatics, 21: 248-256. (2005). Cohen KB, Fox L, Ogren, PV, Hunter L: Corpus design for biomedical natural language processing. Proceedings of the ACL-ISMB workshop on linking biological literature, 108

ontologies and databases, pp. 38-45. Association for Computational Linguistics. (2005). Collier, N., Nobata, C. and Tsujii, J: Extracting the names of genes and gene products with a hidden Markov model. In Proceedings of the 18th International Conference on Computational Lingustics (COLING’2000), Saarbrucken, Germany. (2000). Collier N, Takeuchi K: Comparison of character-level and part of speech features for name recognition in biomedical texts. J Biomed. Inform. 37(6):423-435. (2004). Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A, Mazo I: Extracting human protein interactions from MEDLINE using a full-sentence parser. Bioinformatics, 20: 604-611. (2004). DiGiacomo RA, Kremer JM, Shah DM: Fish-oil dietary supplementation in patients with Raynaud's phenomenon: a double-blind, controlled, prospective study. Am. J. Med. 86:158-164. (1989). Ding J, Berleant D, Nettleton D, Wurtelle E: Mining Medline: abstracts, sentences, or phrases? Pac. Symp. Biocomput. 7: 326-337. (2002). FABLE: http://fable.chop.edu/ Finkel J, Dingare S, Manning CD, Nissim M, Alex B, Grover C. Exploring the 109

boundaries: gene and protein identification in biomedical text. BMC Bioinformatics, 6 Suppl 1:S5. (2005). Friedman C, Hripcsak G, DuMouchel W, Hohnson SB, Clayton PD: Natural language processing in an operational clinical information system. Natural Language Engineering, 1:1-28. (1995). Freimer N, Sabatti C: The human phenome project. Nature Genet, 34: 15-21. (2003). Fundel K, Guttler D, Zimmer R, Apostolakis JA: Simple approach for protein name identification: prospects and limits. BMC Bioinformatics, 6, S15 (2005). The Gene Ontology (GO) project in 2006. Nucleic Acids Res 2006, 34(Database issue):D322-326. GENIA: http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/ (2004). Glenisson P, Anta P, Mathys J, Moreau Y, De Moor B: Evaluation of the vector space representation in text-based gene clustering. Pac. Symp. Biocomput. 8: 391-402. (2003). Glenisson P, Coessens B, Van Vooren S, Mathys J, Moreau Y, De Moor B: TXTGate: profiling gene groups with text-based information. Genome Biol. 5: R43. (2004). 110

Hahn U, Romacker M, Schulz S: MEDSYNDIKATE--a natural language system for the extraction of medical information from findings reports. Int J Med Inform 2002, 67(1-3):63-74. Hakenberg J, Bickel S, Plake C, Brefeld U, Zahn H, Faulstich L, Leser U, Scheffer T: Systematic feature evaluation for gene name recognition. BMC Bioinformatics, 6 Suppl 1:S9. (2005). Hanisch D, Fundel K, Mevissen, HT, Zimmer R, Fluck JP: Rule-based protein and gene entity recognition. BMC Bioinformatics, 6, S14. (2005). Hunter L, Cohen KB: Biomedical language processing: what’s beyond PubMed? Mol. Cell 21:589-594. (2006). Jensen LJ, Saric J, Bork P: Literature mining for the biologist: from information retrieval to biological discovery. Nature Genet. 7: 119-129. (2006). Jessen TK, Lagreid A, Komorowski J, Hovig E: A literature network of human genes for high-throughput analysis of gene expression. Nature Genet. 28: 21-28. (2001). Jin Y, McDonald RT, Lerman K, Mandel MA, Carroll S. Liberman MY, Pereira FC, Winters RS, and White PS. Automated recognition of malignancy mentions in biomedical literature. BMC Bioinformatics, 7: 492. (2006). 111

Kulick S, Bies A, Liberman M, Mandel M, McDonald R, Palmer M, Schein A, Ungar L, Winters S, White P: Integrated annotation for biomedical information extraction. Proc of BioLink (2004). Lafferty J, McCallum A, Pereira F: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proceedings of ICML-01: 282-289. (2001). Lander ES, Linton LM, Birren B, Nusbaum C, etal: Initial sequencing and analysis of the human genome. Nature, 409: 860-921, (2001). Malignancy type definitions: [http://bioie.ldc.upenn.edu/mamandel/annotators/onco/definitions.html] McDonald RT, Winters RS, Mandel, Jin Y, White PS and Pereira F. An entity tagger for recognizing acquired genomic variations in cancer literature. Bioinformatics 22(20): 3249-3251. (2004). McDonald RT, Pereira FN Identifying gene and protein mentions in text using conditional random fields. BMC Bioinformatics, 6 Suppl 1:S6. (2005). McDonald RT, Pereira F, Kulick, Winters RS, Jin Y, White P: Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE. 43rd Annual Meeting 112

of the Association for Computational Linguistics, (2005). MEDLINE: http://www.nlm.nih.gov/databases/databases_medline.html Meldrum D: Automation for genomics, part two: sequencers, microarrays, and future trends. Genome Res, 10:1081-1092, (2000). Mitsumori T, Fation S, Murata M, Doi K, Doi H: Gene/protein name recognition based on support vector machine using dictionary as features. BMC Bioinformatics. 6 Suppl 1:S8. (2005). Muller HM, Kenny EE, Sternberg PW: Textpresso: an ontology-based information retrieval and extraction system for biological literature. PloS Biol. 2, e309. (2004). Novichkova, S., Egorov, S. and Daraselia, N. MedScan, a natural language processing engine for MEDLINE abstracts. Bioinformatics, 19:1699-1706. (2003). Penn BioIE corpus release v0.9 [http://bioie.ldc.upenn.edu] PubMED: http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed Raychaudhuri S, Schutze H, Altman RB: Using text analysis to identify functionally coherent gene groups. Genome Res. 12: 1582-1590. (2002). 113

Rzhetsky A, Iossifov I, Koike T, Krauthammer M, Kra P, Morris M, Yu H, Duboue PA, Weng W, Wilbur JW, Hatzivassiloglou V, Friedman C: GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J. Biomed. Inform. 37: 43-53. (2004). Settles BA: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics, 21: 3191-3192 (2005). Swanson DR: Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspect. Biol. Med., 30:7-18. (1986). Swanson DR: Migrane and magnesium: eleven neglectd connections. Perspect. Biol. Med. 31: 526-557. (1988). Swanson DR: Somatomedin C and arginine: implicit connections between mutually isolated literatures. Perspect. Biol. Med. 33: 157-186. (1990). Tamames J: Text Detective: a rule-based system for gene annotation in biomedical texts. BMC Bioinformatics, 6 Suppl 1:S10. (2005). Tanabe L, Scherf U, Smith LH, Lee JK, Hunter L, Weinstein JN: MedMiner: an internet text-mining tool for biomedical information, with application to gene expression 114

profiling. BioTech. 27: 1210-1217. (1999). Tanabe L, Wilbur W: Tagging gene and protein names in biomedical text, Bioinformatics, 18:1124-1132. (2002). Tanabe L, Xie N, Thom LH, Matten W, Wilbur WJ: GENETAG: a tagged corpus for gene/protein named entity recognition. BMC Bioinformatics 6 Suppl 1:S3. (2005). Temkin JM, Gilder MR: Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics 19:2046-2053. (2003). Torii M, Kamboj S, Vijay-Shanker K: Using name-internal and contextual features to classify biological terms. J Biomed Inform 37(6):498-511. (2004) van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA: A text-mining analysis of the human phenome. Eur J Hum Genet 14(5):535-542. (2006). Weeber M, Kors JA, Mons B: Online tools to support literature-based discovery in the life sciences. Brief. In Bioinfo. 6: 277-286. (2005). Wren JD, Bekeredjian, R., Stewart JA, Shohet, RV and Garner HR: Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics, 20:389-398. (2004). 115

Yakushiji A, Tateisi Y, Miyao Y, Tsujii J: Event extraction from biomedical papers using a full parser. Pac. Symp. Biocomput. 6: 408-419. (2001). Yandell MD, Majoros WH: Genomics and natural language processing. Nat. Rev. Genet., 3:601-610. (2002). Zhou G, Shen D, Zhang J, Su J, Tan S: Recognition of protein/gene names from text using an ensemble of classifiers. BMC Bioinformatics 6 Suppl 1:S7. (2005). 116

Chapter 1. Introduction

by butest

Accessibility

Upload Details

Usage Rights

Statistics

Chapter 1. Introduction Document Transcript