Computational approaches to cell cycle analysis: Current research topics (those I am allowed to talk about)
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PhD Program in Computational Biology, Instituto Gulbenkian de Ciencia, Oeiras, Portugal, April 29-May 2, 2008
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- In an autoimmune mouse model lacking somatic hypermutation (AID deficiency), anti-nuclear responses were delayed and diminished, further supporting this view.
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Critically, the endogenous modification of genes enables the study of their function in a physiological context. It also overcomes some of the artefacts that can result from established techniques such as transgenesis and RNAi, which have mislead researchers with false positives or negatives. Until recently however genome editing required considerable technical expertise, and consequently was a relatively niche pursuit.
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Rapid adoption of the CRISPR/Cas9 system has for example led to a long list of organisms and tissues in which genetic changes have been made with high efficiency. Other technologies such as recombinant adeno-associated virus (rAAV) offer further precision, stimulating the cell’s high-fidelity DNA repair pathways to insert exogenous sequence with unrivalled specificity. Targeting efficiency can be improved still further by using the technologies in combination – genome cutting induced by CRISPR can significantly enhance homologous recombination mediated by rAAV.
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Finally we’ll look at how genome editing is being applied to both basic and translational research, and in both a gene-specific and genome wide manner. For the study of disease associated genes and mutations scientists can now complement wide panels of tumour cells with genetically defined isogenic cell pairs identical in all but precise modifications in their gene of interest. The ease-of-design and efficiency of the CRISPR system is also being exploited for genome wide synthetic lethality screens, facilitating rapid drug target identification with significantly reduced risk of false negatives and off-target false positives. And again, further synergies are achieved when these approaches are combined to look for potential synthetic lethal targets in specific genomic contexts.
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Critically, the endogenous modification of genes enables the study of their function in a physiological context. It also overcomes some of the artefacts that can result from established techniques such as transgenesis and RNAi, which have mislead researchers with false positives or negatives. Until recently however genome editing required considerable technical expertise, and consequently was a relatively niche pursuit.
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Text mining and data integration
This document discusses text mining and data integration techniques used to extract information from biomedical literature and databases. It describes named entity recognition to identify concepts, co-mentioning analysis to find associations between entities, and using these methods along with experimental data and predictions to build integrated networks of genes and proteins and their relationships. These networks are made accessible through web resources that unify data from various sources under common identifiers and provide visualization and programmatic access.
Mining literature and medical records
This document discusses mining literature and medical records using text mining techniques. It summarizes that text mining can be used to extract relevant information from large collections of scientific papers and medical records by using techniques like named entity recognition to identify concepts, information extraction to formalize stated facts, and analyzing co-mentioning of entities to find relationships. Challenges include the unstructured nature of medical records, differences between languages and formats, and privacy concerns when using patient health information. When applied carefully, text mining of literature and medical records can help identify new relationships and insights not captured in existing curated databases or help with medical research questions.
Text mining
Text mining can summarize scientific documents in 3 sentences or less by identifying key entities and relationships. It recognizes concepts like genes, proteins, diseases and extracts facts from text. This extracted information can then be integrated with other data to create more useful resources and provide novel insights through augmented browsing and analysis. Text mining aims to make navigating vast amounts of scientific literature simpler and less boring.
Integration of biomedical literature and databases
Nordic Conference for Scolarly Communication 2008, Scandic Star Hotel, Lund, Sweden, April 21-23, 2008
Text mining
The document discusses text mining and natural language processing techniques for extracting information from scientific documents, including information retrieval, entity recognition, and information extraction. It provides examples of identifying entities like genes and proteins, and extracting relationships between entities, such as regulatory interactions, from text. The techniques discussed aim to formalize facts and biological knowledge from unstructured text into a structured database for further analysis.
Biomedical text mining
This document discusses biomedical text mining techniques used to extract information from scientific papers. It covers named entity recognition to identify concepts like proteins, chemicals and diseases. It also discusses information extraction to formalize facts stated in text, such as interactions between biological components. Techniques include co-mentioning analysis and natural language processing and tools have been applied to large text corpora to aid discovery.
Integration of biomedical literature and databases
2nd European Conference on Scientific Publishing in Biomedicine and Medicine, Rikshospitalet, Oslo, Norway, September 5-6, 2008
Biomedical literature mining (and why we really need open access)
The 28th IATUL annual conference: Global Access to Science - Scientific Publishing for the Future, Royal Institute of Technology (KTH), Stockholm, Sweden, June 11-14, 2007
Biomedical literature mining
This document discusses various methodologies for extracting information from biological literature, including information retrieval, entity recognition, information extraction, and text/data mining. It provides an overview of different approaches like using co-occurrence, natural language processing, and machine learning methods. It also discusses challenges like integrating text with other data types and dealing with issues like ambiguity. Examples of existing text mining tools and their potential applications are also described.
Text mining for protein and small molecule relations
The document discusses using text mining to identify relationships between proteins and small molecules mentioned in biomedical documents. It describes techniques for entity recognition and identification, as well as methods for extracting relationships between entities using co-occurrence analysis and natural language processing. Examples are provided to illustrate how relationships can be identified between proteins mentioned in a sample text passage.
Literature Mining and Systems Biology
The document discusses various techniques for literature mining and systems biology including information retrieval, entity recognition, information extraction, text mining, and integration of text and biological data. It provides examples and status of different methods, from established techniques for information retrieval, entity recognition, and simple information extraction to improving advanced natural language processing-based information extraction and methods for text mining and integration of text and data.
Biological literature mining - from information retrieval to biological disco...
14th International Conference on Intelligent Systems for Molecular Biology, Tutorial, Fortaleza Conference Center, Fortaleza, Brazil, August 6-10, 2006
Mining large-scale data sets on the eukaryotic cell cycle
Data and Text Mining for Integrative Biology, Humboldt University, Berlin, Germany, September 18-22, 2006
Open access - making the most of biomedical literature mining
Berlin 4 Open Access - From Promise to Practice,
Max-Planck-Institute for Gravitational Physics, Potsdam-Golm, Germany, March 29-31, 2006
From Advanced Queries to Algorithms and Graph-Based ML: Tackling Diabetes wit...
This document describes 3 use cases for a pharmaceutical knowledge graph created by the Data and Knowledge Management team at the German Center for Neurodegenerative Diseases (DZD). The knowledge graph connects heterogeneous and unstructured data using a graph database. The 3 use cases are: 1) Mapping identifiers to handle molecular entities, 2) Finding connected information by querying relationships, and 3) Annotating and enriching text with natural language processing and ontologies. The knowledge graph allows for advanced queries, graph algorithms, and machine learning techniques to gain new insights.
Similar to Computational approaches to cell cycle analysis: Current research topics (those I am allowed to talk about) (20)
Literature mining and large-scale data integration
Literature mining and large-scale data integration
Integration of biomedical literature and databases
Integration of biomedical literature and databases
Integration of biomedical literature and databases
Integration of biomedical literature and databases
Biomedical literature mining (and why we really need open access)
Biomedical literature mining (and why we really need open access)
Text mining for protein and small molecule relations
Text mining for protein and small molecule relations
Biological literature mining - from information retrieval to biological disco...
Biological literature mining - from information retrieval to biological disco...
Mining large-scale data sets on the eukaryotic cell cycle
Mining large-scale data sets on the eukaryotic cell cycle
Open access - making the most of biomedical literature mining
Open access - making the most of biomedical literature mining
From Advanced Queries to Algorithms and Graph-Based ML: Tackling Diabetes wit...
From Advanced Queries to Algorithms and Graph-Based ML: Tackling Diabetes wit...
More from Lars Juhl Jensen
One tagger, many uses: Illustrating the power of dictionary-based named entit...
This document summarizes a Twitter thread discussing the uses of a dictionary-based named entity recognition tool called Tagger. Tagger can recognize genes, proteins, diseases and other biomedical entities. It is open source, runs quickly processing over 1000 abstracts per second, and achieves 70-80% recall and 80-90% precision. Tagger has been applied to tasks like identifying drug-disease associations, adverse drug events, and protein-protein interactions. It is available as a Docker container or web service.
One tagger, many uses: Simple text-mining strategies for biomedicine
The document summarizes a text mining tool called a tagger that can be used for named entity recognition in biomedical texts. It recognizes genes, proteins, chemicals, diseases, and other entities. The tagger is open source, runs quickly at over 1000 abstracts per second, and has 70-80% recall and 80-90% precision. It comes with Python and Docker implementations and can be accessed via a web service. It is useful for tasks like extracting functional associations from literature and electronic health records.
Extract 2.0: Text-mining-assisted interactive annotation
This document describes Extract 2.0, a text-mining tool that can assist with interactive annotation of documents. It uses dictionary-based tagging to identify relevant entities like genes and diseases. It achieves 70-80% recall and 80-90% precision on entity extraction and was evaluated in BioCreative challenges where it received positive feedback from curators. The tool is open source and available as a web service or Python wrapper.
Network visualization: A crash course on using Cytoscape
This document discusses using Cytoscape, a network analysis tool, to import and visualize networks from STRING and STITCH databases. It provides three examples of networks created from literature and disease queries, demonstrating how to import networks and tables, apply node attributes and visual styles, perform enrichment analysis, and more.
STRING & STITCH : Network integration of heterogeneous data
The document discusses STRING and STITCH, two online databases that integrate data on protein-protein interactions, pathways, and functional associations from various sources. STRING collects data on over 9.6 million proteins and 430 thousand chemicals from sources like text mining, experimental assays, and co-expression analyses. It aims to provide a comprehensive global view of known and predicted protein associations. STITCH also integrates interaction data but focuses more on chemical-protein interactions. Both databases provide user-friendly web interfaces for browsing and visualizing interaction networks.
Biomedical text mining: Automatic processing of unstructured text
1) Lars Juhl Jensen discusses biomedical text mining and automatic processing of unstructured text such as patent literature, grant proposals, FDA product labels, and electronic medical records.
2) Named entity recognition is used to identify genes/proteins, chemical compounds, diseases, and other entities in text through comprehensive dictionaries and flexible matching rules that account for variations.
3) Relation extraction uses natural language processing techniques like part-of-speech tagging and sentence parsing along with manually crafted rules and machine learning to identify implicit relations between entities in text such as transcription factor targets, kinase substrates, and protein-protein interactions.
Medical network analysis: Linking diseases and genes through data and text mi...
The document summarizes the work of Lars Juhl Jensen and others on medical network analysis and linking diseases and genes through data and text mining of electronic health records. It discusses how they have used Danish national health registries containing data on over 6 million patients and 119 million diagnoses over 14 years to study disease trajectories and comorbidities. It also describes how they have developed methods to integrate data from various sources to generate networks linking diseases and genes.
Network Biology: A crash course on STRING and Cytoscape
This document provides an overview of STRING, a protein-protein association database, and Cytoscape, a network visualization tool. It describes how STRING contains functional associations between proteins derived from genomic context, co-expression and curated databases. Cytoscape can import STRING networks and external data to map onto nodes. It offers visualization of networks through layouts and attributes, and analysis through clustering, selection filters and enrichment. The document recommends using these tools together to explore protein association networks.
Cellular networks
This document discusses different approaches to visualizing cellular networks and the molecular interactions between proteins. It notes that there are many different types of data that could be shown, such as protein names, functions, localization, expression, modifications, and interaction types. However, it is impossible to show all this information at once. The document recommends using different visualizations like force-directed layouts to distribute proteins in 2D or lining up interactions in 1D. It acknowledges open challenges like showing time-course data and modification sites. In the end, the document thanks several researchers who have contributed to mapping and visualizing cellular networks.
Cellular Network Biology: Large-scale integration of data and text
The document discusses various community resources and software tools for integrating large-scale data and text, including STRING for protein networks, STITCH for chemical networks, COMPARTMENTS for subcellular localization, TISSUES for tissue expression, and DISEASES for disease associations. It provides an overview of text mining techniques used to extract information from literature to build networks in these resources. The presenter demonstrates the Cytoscape App which can import and analyze networks from STRING, perform queries, and analyze subcellular localization, tissue expression, and disease enrichment.
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
This document discusses statistical methods for analyzing high-throughput biomedical screens and common pitfalls. It introduces several statistical tests such as t-tests, ANOVA, Fisher's exact test, and the Mann-Whitney U test. It also discusses challenges like multiple testing, resampling techniques, and biases that can occur like studiedness bias and abundance bias in big data analyses. Controlling false discovery rates and considering effect sizes are recommended over solely relying on p-values to determine biological significance.
STRING & related databases: Large-scale integration of heterogeneous data
The document discusses the STRING database, which integrates heterogeneous biological data to generate association networks for proteins. It describes how STRING collects and connects curated knowledge, experimental data, and predicted interactions from genomic context, co-expression and text mining. The document also outlines exercises for users to explore protein-protein associations in STRING and related databases that integrate data on subcellular localization, tissue expression, and disease associations.
Tagger: Rapid dictionary-based named entity recognition
Tagger is a named entity recognition tool that can process over 1000 abstracts per second using a dictionary-based approach. It achieves 70-80% recall and 80-90% precision using comprehensive dictionaries, expansion rules, and a curated blacklist to identify entity types like genes, proteins, chemicals, and diseases. The tool has a C++ engine, is inherently thread-safe, and includes interactive annotation, Python wrappers, and a REST API.
Network Biology: Large-scale integration of data and text
Lars Juhl Jensen leads a group that conducts large-scale integration of biological and medical data using proteomics, text mining, and medical data mining. The group develops protein interaction networks, disease networks, and association networks. They collaborate internationally on projects involving over 9.6 million proteins and 2000 genomes. The group works to integrate data from many sources in different formats to build comprehensive networks and knowledgebases, and also mines biomedical text to link genes and proteins with diseases.
Medical text mining: Linking diseases, drugs, and adverse reactions
This document discusses medical text mining and linking diseases, drugs, and adverse reactions. It describes using text mining on clinical narratives in Danish to recognize named entities like drugs and diseases, identify relationships between them like adverse drug reactions, and discover new ADRs. The goal is to generate structured data on topics like comorbidities, diagnosis trajectories, and reimbursement to supplement limited structured data and help busy doctors by analyzing large amounts of unstructured text.
Network biology: Large-scale integration of data and text
The document discusses network biology and large-scale data integration. It describes protein-protein interaction networks like STRING that integrate data from curated knowledge, experiments, and predictions. It provides exercises to explore the human insulin receptor (INSR) in STRING, examining the types of evidence that support its interaction with IRS1. It also introduces other integrated networks like STITCH for chemicals and COMPARTMENTS for subcellular localization. Natural language processing techniques like named entity recognition, information extraction, and semantic tagging are used to integrate text data from the literature into these interaction networks.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining to link diseases, drugs, and adverse reactions. It describes using structured data from Danish central registries and unstructured data from hospital electronic health records. Named entity recognition is used to extract diseases, drugs, and adverse reactions from free text clinical notes written in Danish. Hand-crafted rules are developed to identify relationships between extracted entities like adverse drug reactions. This allows estimating frequencies of known adverse drug reactions and discovering new adverse drug reactions by analyzing diagnosis trajectories and medication information.
Cellular Network Biology
This document discusses cellular network biology and summarizes several key papers on topics like proteome analysis using mass spectrometry, integrating protein network and experimental data, challenges with different biological databases having varying formats and quality, and using natural language processing techniques like named entity recognition and relation extraction to analyze medical text for information like diagnosis trajectories and adverse drug reactions.
Network biology: Large-scale integration of data and text
This document discusses natural language processing (NLP) techniques for extracting information from biomedical literature and integrating it with network and interaction data. It describes how NLP is used to identify entities like genes and proteins, extract relationships between entities, and integrate this text-mined information with existing interaction networks from databases like STRING to expand knowledge of protein interactions, complexes, pathways and associations with diseases. The document provides examples of using NLP analysis on sentences and the STRING and Tissues databases to explore tissue specificity and disease relationships for insulin and the insulin receptor.
Biomarker bioinformatics: Network-based candidate prioritization
The document discusses three parts of biomarker bioinformatics: data integration from multiple databases, text mining of scientific literature, and using that integrated data to prioritize biomarker candidates. It describes combining data on 9.6 million proteins from curated databases, using text mining to extract named entities from over 10,000 papers, and then using network and heat diffusion approaches to rank candidates based on evidence in the integrated data. The goal is to help identify new biomarker candidates from large amounts of biological data.
More from Lars Juhl Jensen (20)
One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Simple text-mining strategies for biomedicine
One tagger, many uses: Simple text-mining strategies for biomedicine
Extract 2.0: Text-mining-assisted interactive annotation
Extract 2.0: Text-mining-assisted interactive annotation
Network visualization: A crash course on using Cytoscape
Network visualization: A crash course on using Cytoscape
STRING & STITCH : Network integration of heterogeneous data
STRING & STITCH : Network integration of heterogeneous data
Biomedical text mining: Automatic processing of unstructured text
Biomedical text mining: Automatic processing of unstructured text
Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...
Network Biology: A crash course on STRING and Cytoscape
Network Biology: A crash course on STRING and Cytoscape
Cellular Network Biology: Large-scale integration of data and text
Cellular Network Biology: Large-scale integration of data and text
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
STRING & related databases: Large-scale integration of heterogeneous data
STRING & related databases: Large-scale integration of heterogeneous data
Tagger: Rapid dictionary-based named entity recognition
Tagger: Rapid dictionary-based named entity recognition
Network Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and text
Medical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactions
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
Biomarker bioinformatics: Network-based candidate prioritization
Biomarker bioinformatics: Network-based candidate prioritization
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While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Computational approaches to cell cycle analysis: Current research topics (those I am allowed to talk about)
- 1. Current research topics (those I am allowed to talk about) Lars Juhl Jensen EMBL Heidelberg
- 3. why?
- 5. too much to read
- 8. ad hoc retrieval
- 10. “ yeast AND cell cycle”
- 11. stemming
- 12. yeast / yeasts
- 14. yeast / S. cerevisiae
- 19. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5-dependent Swe1 hyperphosphorylation and degradation
- 20. no tool will find it
- 23. Mitotic cyclin ( Clb2 )-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5 -dependent Swe1 hyperphosphorylation and degradation
- 26. CDC28
- 27. Cdc28p
- 28. disambiguation
- 29. hairy
- 30. SDS
- 31. APC
- 32. Cdc2
- 35. still too much to read
- 38. co-mentioning
- 41. NLP Natural Language Processing
- 43. Mitotic cyclin ( Clb2 )-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5 -dependent Swe1 hyperphosphorylation and degradation
- 46. text mining
- 50. fish oil
- 52. temporal trends
- 54. buzzwords
- 66. genomic context
- 75. not comparable
- 76. benchmarking
- 77. calibrate vs. gold standard
- 80. spread over many species
- 84. P = 1-(1-P 1 ) . (1-P 2 ) . (1-P 3 ) …
- 94. kinase families
- 95. overprediction
- 96. context
- 97. co-activators
- 98. scaffolders
- 101. NetworKIN
- 103. benchmarking
- 106. ATM signaling