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Luciano informs healthcare_2015 Nashville, TN USA July 30 2015

The document discusses the concept of health web observatories and their potential to improve health outcomes through health web science, emphasizing the transformative impact of the internet on healthcare behaviors and practices. It outlines key objectives for modern healthcare, including the integration of technology and web resources, and the importance of data sharing and semantic interoperability. The document highlights the role of web science in understanding human behavior's influence on health outcomes and advocates for a collaborative approach to advance health web science initiatives.

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 Health Web Observatories: Creating Preferable Health Outcomes through Health Web Science Joanne S. Luciano, PhD Predictive Medicine, Inc., Belmont, MA (predmed.com) Rensselaer Polytechnic Institute, Troy, NY 30 July 2015 INFORMS Healthcare Conference 2015 Nashville, Tennessee, USA 7/30/15 1
PRESENTER Joanne S. Luciano Enable Health and Wellbeing through Knowledge Technology BS, MS Computer Science PhD Cognitive and Neural Systems (Computational Neuroscience) Wang Labs Harvard Medical School MITRE Lotus Development Predictive Medicine, Inc. Rensselaer Polytechnic Institute GE Global Research Labs Interests Flying planes, rocks: climbing, balancing and photographing them Community BioPathways Consortium, BioPAX, W3C HCLSIG, Yosemite Project, FIBO Email: jluciano@rpi.edu jluciano@predmed.com Always open to exploring opportunities. 7/30/15 2
Multidisciplinary International Team 7/30/15 3 Grant Cumming, Medical Doctor, NHS Grampian, Honorary Professor, University of the Highlands and Islands, AB24 2ZN, Aberdeen, United Kingdom, grant.cumming@nhs.net Tara French,Research Fellow, Institute of Design Innovation, The Glasgow School of Art, Horizon Scotland, Digital Health Institute, Forres IV36 2AB, United Kingdom, tara.french@dhi-scotland.com Eva Kahana,Distinguished University Professor and The Pierce T. and Elizabeth D. Robson Professor of the Humanities, Case Western Reserve University, Mather Memorial Building 231B, Cleveland OH 44106, United States of America, eva.kahana@case.edu David Molik,Computational Developer, Cold Spring Harbor Laboratories, One Bungtown Road, Cold Spring Harbor NY 11724, United States of America, dmolik@cshl.edu
Objectives —  Formulating Healthcare for the 21st Century —  Are we where we should be? —  What’s missing? —  How do we use the Web? —  How can we use the Web? —  How do we know what will work? —  What are the tools, technologies, and resources needed? —  How do we evaluate effectiveness? 7/30/15 4
Brendan Ashby Master’s Thesis (RPI) Actively SEEKING FUNDING Nightingale Research to Practice Timeline(earlier work: 10 years in Software Research & Development and Product Development) 20091993 World Congress on Neural Networks, July 11-15, 1993, Portland, Oregon SIG Mental Function and Dysfunction Sam Levin Jackie Samson, Mc Lean Hospital Depression Research 1996 1995 20081994 Patents Sold to Advanced Biological Laboratories Belgium Patents Offered at Ocean Tomo Auction Chicago, IL US Patent No. 6,317,73 Awarded US Patents No. 6,063,028 Awarded 2001 2000 PhD Thesis Proposal Approved Workshop Neural Modeling of Cognitive and Brain Disorders BioPAX ? Linked Data W3C HCLS BioDASH EPOS 2006 EMPWR Poster Presented ISMB 1997 PSB 1998 1997 2010 Rensselaer (RPI) 2011 2012 2013 U Pitt Greg Siegle Depression Collaboration Yuezhang Xiao Master’s Thesis (RPI) Failed to get Funding for Proactive Multimodal Depression Treatment Health Web Science 7/30/15 5 2014 2015 Is 15-20 years too long to get from research to practice?
Healthcare Singularity and the age of Semantic Medicine http://research.microsoft.com/en-us/collaboration/fourthparadigm/4th_paradigm_book_part2_gillam.pdf 2,300 years after the first report of angina for the condition to be commonly taught in medical curricula, modern discoveries are being disseminated at an increasingly rapid pace. 7/30/15 6
Healthcare Singularity and the age of Semantic Medicine http://research.microsoft.com/en-us/collaboration/fourthparadigm/ 4th_paradigm_book_part2_gillam.pdf Focusing on the last 150 years, the trend still appears to be linear, approaching the axis around 2025. 7/30/15 7
Times have changed —  Aging population (end of life costly) —  More people with chronic illnesses (increased cost) —  The end of the blockbuster era (decrease in revenues, increase in drug development cost) —  Need lower drug development cost —  Personalized Medicine (right treatment to the right patient at the right time) —  Improved patient response to treatment (Evidence Based) —  Web and Mobile —  The technology (ubiquitous, monitor) —  Patient engagement increasing 8 Photos: http://www.flickr.com/photos/sepblog/4014143391/ http://allthingsd.com/files/2013/07/photo-12.jpg 7/30/15
Data Driven Medicine: 3 Shifts in thinking and practice: — Data, Not Programs (reuse!) — Sharing, Not Hoarding (or hiding) — Personal, Not (only) Population 9 7/30/15
Data Sharing 10http://www.youtube.com/watch?v=N2zK3sAtr-4 7/30/15
7/30/15 11 Health Web Observatories: Creating Preferable Health Outcomes through Health Web Science
7/30/15 12 The impact of the personal computer and internet on an individuals potential to influence society.
7/30/15 13 Health Web Science recognizes the revolutionary impact of the Internet, made possible through the Web, with the potential to change health behaviors and health care worldwide. This impact on changing the practice of medicine can be considered in three areas: power, experience and speed.
7/30/15 14 Web Science (WS) Web Science is about investigating how human behavior co-constitutes the Web. People who impose regulations, engineer the Web, produce content, or even just click on links change the Web how other people will see it. Vice versa, what people see and do on the Web will change their behavior. Web Science is about understanding this cycle. SteffenStaab
7/30/15 15 1/3 world’s population use the Web [1] 80% look for health information online [2] •  Studies impact of the Web on health and wellbeing •  Aims towards a preventative, participatory, personalized, and predictive (P4) model of healthcare. •  Posits P4 can be achieved by the leveraging of the Web’s data, resources and nature. •  Studies the evolving social, political, economic, policy health related questions that emerge as a result of the use of the Web. Health Web Science (HWS) [1] Miniwatts Marketing Group 2012 [2] California Healthcare Foundation, Fox, S. 2011
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7/30/15 17 The World Wide Web •  Directly influences conscious behavior (Kahneman, System 2) through imparting information •  Indirectly influences unconscious behavior (Kahneman, System 1) through social interactions •  “co-conscious” interactions are the emergent collective consciousness of the networ The Web and Human Behavior Influence Health Outcomes HWS seeks to understand the dynamics of these behavioral influences in order to support users in achieving better health outcomes
7/30/15 18 Instruments for Web Study – what works and what doesn’t, i.e. when to use technology, policy, transparency? •  Enable data to be found •  Make the metadata available for use by others •  Study the data in context using metadata •  Aggregation and presentation of observations enable a feedback mechanism for preferable futures. A health Web Observatory is a system that gathers and links to health data on the Web in order to answer questions about the Web, the users of the Web and the way that they affect each other within the context of healthcare. Health Web Observatory (HWO)
How? Technologies Needed to enable Health Web Science and the vision for 21st Century Medicine It’s all about the meaning! — Semantic Enabling: Web Observatories — Semantic Interoperability: — Shared Meaning: Yosemite Project — Inference: Ontologies and OWL — Linked Data: RDF, HTTP, URIs as terms 7/30/15 19
Enabling Web Observatories 7/30/15 20
How? Technologies Needed to enable Health Web Science and the vision for 21st Century Medicine It’s all about the meaning! — Semantic Enabling: Web Observatories — Semantic Interoperability: — Shared Meaning: Yosemite Project — Inference: Ontologies and OWL — Linked Data: RDF, HTTP, URIs as terms 7/30/15 21
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Unified Medical Language System Knowledge Sources The UMLS has three tools, called the UMLS Knowledge Sources: —  Metathesaurus: Terms and codes from many vocabularies, including CPT®, ICD-10-CM, LOINC®, MeSH®, RxNorm, and SNOMED CT® —  Semantic Network: Broad categories (semantic types) and their relationships (semantic relations) —  SPECIALIST Lexicon and Lexical Tools: Natural language processing tools 7/30/15 23
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How? Technologies Needed to enable Health Web Science and the vision for 21st Century Medicine It’s all about the meaning! — Semantic Enabling: Web Observatories — Semantic Interoperability: — Shared Meaning: Yosemite Project — Inference: Ontologies and OWL — Linked Data: RDF, HTTP, URIs as terms 7/30/15 27
Ontology Spectrum http://www.mkbergman.com/wp-content/themes/ai3v2/ images/2007Posts/070501d_SemanticSpectrum.png Strong Semantics Weak Semantics 7/30/15 28
Ontology Spectrum Reuse of terminological resources for efficient ontological engineering in Life Sciences by  Jimeno-Yepes, Antonio;  Jiménez-Ruiz, Ernesto;  Berlanga-Llavori, Rafael;  Rebholz-Schuhmann, Dietrich Journal: BMC Bioinformatics  Vol.  10  Issue  Suppl 10 DOI: 10.1186/1471-2105-10-S10-S4 http://www.mkbergman.com/wp-content/themes/ai3v2/ images/2007Posts/070501d_SemanticSpectrum.png Existing formalisms Strong Semantics Weak Semantics 7/30/15 29
Application vs. Reference Ontology Reference Ontology —  Intended as an authoritative source —  True to the limits of what is known (this changes!) —  Used by others —  Application Ontology —  Built to support a particular application (use case) —  Reused rather than define terms —  Skeleton structure to support application —  Terms defined refine or create new concepts directly or through new classes based on inference http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf 7/30/15 30
Healthcare and Life Science Goal: a suite of orthogonal interoperable reference ontologies Barry Smith U Buffalo, NCBO From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 The Open Biological and Biomedical Ontologies http://www.obofoundry.org 7/30/15 31
How? Technologies Needed to enable Health Web Science and the vision for 21st Century Medicine It’s all about the meaning! — Semantic Enabling: Web Observatories — Semantic Interoperability: — Shared Meaning: Yosemite Project — Inference: Ontologies and OWL — Linked Data: RDF, HTTP, URIs as terms 7/30/15 32
The Open Biological and Biomedical Ontologies From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 http://www.obofoundry.org 7/30/15 33
Translational Medicine Ontology Overview of selected types, subtypes (overlap) and existential restrictions (arrows) in the Translational Medicine Ontology. 7/30/15 34The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside Luciano et al. Journal of Biomedical Semantics 2011, 2(Suppl 2):S1 http://www.jbiomedsem.com/content/2/S2/S1 Bridge the Gap Between “Bench and Bedside”
Translational Medicine Knowledge BaseTranslational Medicine Ontology with mappings to ontologies and terminologies listed in the NCBO BioPortal. The TMO provides a global schema for Indivo-based electronic health records (EHRs) and can be used with formalized criteria for Alzheimer’s Disease. The TMO maps types from Linking Open Data sources. 7/30/15 35
Individuals, Not Populations 36 Photo: http://www.flickr.com/photos/sepblog/4014143391/ http://safety-code.org/ Quickly retrieve pharmacogenomic markers of patients when needed No central storage of data is necessary, giving patients full control over their personal health information. 7/30/15
Application Ontology Influenza Ontology http://www-test.ebi.ac.uk/industry/Documents/workshop-materials/DiseaseOntologiesAndInformation190608/The%20Influenza %20Infectious%20Disease%20Ontology%20(I-IDO)%20-%20Joanne%20Luciano.pdf 7/30/15 37
Application Ontology Influenza Ontology http://www-test.ebi.ac.uk/industry/Documents/workshop-materials/DiseaseOntologiesAndInformation190608/The%20Influenza %20Infectious%20Disease%20Ontology%20(I-IDO)%20-%20Joanne%20Luciano.pdf 7/30/15 38
Conclusion Creating Preferable Health Outcomes through Health Web Science —  Web Science —  Health Web Observatories as web tools —  Semantic Technologies —  Standards and Interoperability Web Observatories are VERY EARLY STAGE in HEALTH —  Health Web Sciences Needs your help! 7/30/15 39 https://www.baby-connect.com/images/baby2.gif https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcTFXOU0CsGM8pddeiadAbtTirgIv- _3KeaL_fhKIYYFAMPEOTy3
Thank You! 7/30/15 40
What is UMLS? The UMLS, or Unified Medical Language System Enables Interoperability between computer systems —  Files —  Software that brings together many health and biomedical —  vocabularies and standards You can use the UMLS to enhance or develop applications, such as electronic health records, classification tools, dictionaries and language translators. http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf http://www.nlm.nih.gov/research/umls/quickstart.html 7/30/15 41
Unified Medical Language System Access to the UMLS The UMLS Terminology Services (UTS) provides three ways to access the UMLS: —  Web Browsers You can search the data through UTS applications: —  Metathesaurus Browser - Retrieve UMLS concept information, including CUIs, semantic types, and synonymous terms. —  Semantic Network Browser - View the names, definitions, and hierarchical structure of the Semantic Network. —  Local Installation download, customize and load into your database system, or browse your data using the MetamorphoSys RRF browser. —  Web Services APIs You can use NLM’s application programming interfaces (APIs) to query the UMLS data within your own application. 7/30/15 42
Unified Medical Language System License Required —  Request a license (FREE) —  Sign up for a UMLS Terminology Services (UTS) account. —  UMLS licenses are issued only to individuals —  NLM is a member of the IHTSDO (owner of SNOMED CT), and there is no charge for SNOMED CT use in the United States and other member countries. Some uses of the UMLS may require additional agreements with individual terminology vendors. The UTS account allows you to browse, download, and query the UMLS. 7/30/15 43
Unified Medical Language System Use UMLS to link health information, medical terms, drug names, and billing codes across different computer systems. Some examples: —  Linking terms and codes between doctor, pharmacy, and insurance company —  Patient care coordination among several departments within a hospital —  SNOMED, ICD-9, LOINC, RxNorm – clinical setting, more about this later in the next part of the tutorial The UMLS has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research. http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf 7/30/15 44
Overview Introduction (10 minutes) 1.  Background 1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout 2.  Examples: (40 minutes) 1.  Reference Ontology Examples 1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5) 2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10) 3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences 7/30/15 45
Examples 3 Reference Ontology Examples — UMLS – High level across biomedicine — BioPAX – Mid level – biological pathways — Gene Ontology (“GO”) – Gene annotation 2 Application Ontology Example — Influenza Ontology — Translational Medicine Ontology 7/30/15 46
The Open Biological and Biomedical Ontologies From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 http://www.obofoundry.org 7/30/15 47
BioPAX Biological PAthway eXchange An abstract data model for biological pathway integration Initiative arose from the community 487/30/15
49 Metabolic PathwaysBioPAX Level 1 Biological Pathways of the Cell BioPAX A series of chemical reactions, catalyzed by enzymes The products of one are the reactants of the next e.g. Conversion, Transport 7/30/15
50 BioPAX Level 2 BioPAX Biological Pathways of the Cell Cells are complex systems whose physiology is governed by an intricate network of Molecular Interactions (MIs) of which a relevant subset are protein–protein interactions (PPIs). Molecular Interaction Networks http://www.estradalab.org/research/ 7/30/15
51 BioPAX Biological Pathways of the Cell Molecular Interaction Networks http://www.estradalab.org/research/ Human Protein Interaction Network (PIN) 7/30/15 BioPAX Level 2
Biological Pathways of the Cell Adapted from Cell Signalling Biology - Michael J. Berridge - www.cellsignallingbiology.org - 2012 and http://www.hartnell.edu/tutorials/biology/signaltransduction.html 52 Signaling Pathways BioPAX Level 3 BioPAX Signaling molecules trigger cellular responses. Molecules bind to the cell surface causing a cascade of activation Reactions A activates B activates C…. 7/30/15
53 Gene Regulation BioPAX Biological Pathways of the Cell The modulation of any of the stages of gene expression that control: which genes are switched on and off when, how long, and how much Gene regulation may occur many stages: Transcription Post-transcriptional modification RNA transport Translation mRNA degradation Post-translational modifications among many others (more recently discovered!) http://www.biology-online.org/dictionary/Gene_regulation http://en.wikipedia.org/wiki/Regulation_of_gene_expression 7/30/15
54 Metabolic Pathways Molecular Interaction Networks Signaling Pathways Gene Regulation BioPAX Level 1 BioPAX Level 2 BioPAX Level 3 BioPAX Level 4 BioPAX What’s a pathway? Depends on who you ask! Biological Pathways of the Cell 7/30/15
BioPAX Ontology 55 Level 1 v1.0 (July 7th, 2004) parts how the parts are known to interact a set of interactions 7/30/15
BioPAX Biochemical Reaction 56 phosphoglucose isomerase 5.3.1.9 OWL (schema) Instances (Individuals) (data) 7/30/15
Before BioPAX With BioPAX Common “computable semantic” enables scientific discovery >200 DBs and tools Database Application User BioPAX - Simplify 7/30/15 57
Examples 3 Reference Ontology Examples — UMLS – High level across biomedicine — BioPAX – Mid level – biological pathways — Gene Ontology (“GO”) – Gene annotation 2 Application Ontology Example — Influenza Ontology — Translational Medicine Ontology 7/30/15 58
The Open Biological and Biomedical Ontologies From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 http://www.obofoundry.org 7/30/15 59
Gene Ontology (GO) Standard representations: —  Gene and gene product attributes —  Across species and databases 7/30/15 60 [1] Rhee, S.Y, Wood, V., Dolinski, K. and Draghici, S. 2008. Use and misuse of the gene ontology annotations. Nature Reviews Genetics 9:509-515. [2] http://people.oregonstate.edu/~knausb/rna_seq/annot.pdf Structured controlled vocabularies organized as 3 independent Ontologies —  Molecular Interactions —  Biological Processes —  Cellular Location
Gene Ontology Two Key Uses: —  Resource: to look up genes with similar functionality or location within the cell to help characterize the function of a sequence or structure —  Use to annotate genomes to enable the analysis of the genome through the annotation terms. 7/30/15 61
Gene Ontology Evidence Codes Adapted from: http://people.oregonstate.edu/~knausb/rna_seq/annot.pdf Rhee, S.Y, Wood, V., Dolinski, K. and Draghici, S. 2008. Use and misuse of the gene ontology annotations. Nature Reviews Genetics 9:509-515. See also: http://www.geneontology.org/GO.evidence.shtml Manually-assigned evidence codes fall into Four categories: Experimental Computational analysis Author statements, Curatorial statements 7/30/15 62 Inferred from Electronic Annotation (IEA) is not assigned by a curator.
Sequence Ontology Sequence Ontology (SO) ‘terms and relationships used to describe the features and attributes of biological sequence.’ (E.g., binding_site, exon, etc.) SO http://www.sequenceontology.org/ sequence_attribute feature_attribute polymer_attribute sequence_location variant_quality sequence_feature junction region sequence_alteration sequence_variant functional_variant structural_variant Relationship (lots!) 7/30/15 63 (snuck this one in as another example)
Overview Introduction (10 minutes) 1.  Background 1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout 2.  Examples: (40 minutes) 1.  Reference Ontology Examples 1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5) 2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10) 3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences 7/30/15 64
Examples 3 Reference Ontology Examples — UMLS – High level across biomedicine — BioPAX – Mid level – biological pathways — Gene Ontology (“GO”) – Gene annotation 2 Application Ontology Example — Influenza Ontology — Translational Medicine Ontology 7/30/15 65
Application vs. Reference Ontology Reference Ontology —  Intended as an authorative source —  True to the limits of what is known —  Used by others —  Application Ontology —  Built to support a particular application (use case) —  Reused rather than define terms —  Skeleton structure to support application —  Terms defined refine or create new concepts directly or through new classes based on inference http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf 7/30/15 66
Application Ontology Influenza Ontology http://www-test.ebi.ac.uk/industry/Documents/workshop-materials/DiseaseOntologiesAndInformation190608/The%20Influenza %20Infectious%20Disease%20Ontology%20(I-IDO)%20-%20Joanne%20Luciano.pdf 7/30/15 67
Application Ontology Influenza Ontology http://www-test.ebi.ac.uk/industry/Documents/workshop-materials/DiseaseOntologiesAndInformation190608/The%20Influenza%20Infectious%20Disease %20Ontology%20(I-IDO)%20-%20Joanne%20Luciano.pdf 7/30/15 68
Overview Introduction (10 minutes) 1.  Background 1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout 2.  Examples: (40 minutes) 1.  Reference Ontology Examples 1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5) 2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10) 3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences 7/30/15 69
Examples 3 Reference Ontology Examples — UMLS – High level across biomedicine — BioPAX – Mid level – biological pathways — Gene Ontology (“GO”) – Gene annotation 2 Application Ontology Example — Influenza Ontology — Translational Medicine Ontology 7/30/15 70
Overview Introduction (10 minutes) 1.  Background 1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout 2.  Examples: (40 minutes) 1.  Reference Ontology Examples 1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5) 2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10) 3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences 7/30/15 71
Best Practices Semantic Web Methodology & Technology Development Process Fox, Peter & McGuinness, Deborah 2008 http://tw.rpi.edu/web/doc/TWC_SemanticWebMethodology 7/30/15 72
Generalized Ontology Evaluation Framework (GOEF) 73 Two stages: 1.  Recast use case into its components: Three Levels of Evaluation 2.  Evaluate components using objective metrics
BioPortal http://bioportal.bioontology.org/ Provides access to commonly used biomedical ontologies and to tools for working with them. BioPortal allows you to —  Browse —  the library of ontologies —  mappings between terms in different ontologies —  a selection of projects that use BioPortal resources —  Search —  biomedical resources for a term —  for a term across multiple ontologies —  Receive recommendations —  on which ontologies are most relevant for a corpus —  Annotate text —  with terms from ontologies All information available through the BioPortal Web site is also available through the NCBO Web service REST API. Please see REST API documentation for more information. http://www.bioontology.org/wiki/index.php/NCBO_REST_services 7/30/15 74
Conferences 7/30/15 75 Conference on Semantics in Health Care and Life Sciences (CSHALS) Semantic web applications and tools for life sciences (SWAT4LS) Edinburgh 2013
Conclusion Tutorial sources —  BioPortal —  W3C HCLSIG Consortia to join —  W3C HCLSIG —  OpenPHACTS —  Identifiers.org —  Pistoia Alliance —  BioPAX (check for new name) 7/30/15 76
THANK YOU! RPI Tetherless World Constellation RPI Web Science Research Center Predictive Medicine, Inc. W3C Health Care & Life Science SIG BioPathways Consortium BioPAX Harvard Medical School, Mass General Hospital Abha Moitra, Petr Haug, Larry Hunter, Bob Powers, Scott Marshall, Matthias Samwald, Michel Dumontier, Ted Slater, Eric Neumann, Lynette Hirschman, Lynn Schriml, Rick Lathrop and many many others! NSF, NIH, NIST, IEEE and many others! 7/30/15 77
Backup Slides 7/30/15 78
HL-7 and RIM HL-7 and RIM: http://www.w3.org/2013/HCLS-tutorials/ RIM/#%286%29 —  RDF RIM Tutorial Eric Prud'hommeaux, <eric@w3.org> —  Basic understanding of the structure of how data written in HL7's RIM can be expressed in RDF. —  It is not a substitute for HL7's documentation, but instead the author's notion of a quick way to familiarize oneself with the concepts and terms used in the RIM and how the graph structure of RDF is a natural way to represent this data. Copyright © 2013 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply. 7/30/15 79
Personalized Medicine Components •  Understand disease heterogeneity —  Comprehend disease progression •  Determine genetic and environmental contributors —  Create treatments against relevant targets —  drugs against relevant targets (molecular structures) —  Yoga against stress —  Exercise against obesity —  Elimination against food intolerance or allergy •  Develop markers to predict response •  Identify concrete endpoints to measure response 7/30/15 80
Scope Ontology Uses —  Knowledge Management —  Annotate data (such as genomes) —  Access information (search, find, and access) —  Map across ontologies relate —  Data integration and exchange —  Model dynamic cellular processes —  Identify Drug Interactions —  Decision support —  SafetyCodes —  Diabetic Care —  Lab Alerts (Bodenreider YBMI 2008) http://themindwobbles.wordpress.com/2009/05/04/olivier-bodenreider-nlm- best-practices-pitfalls-and-positives-cbo-2009/ 7/30/15 81
Unified Medical Language System Metathesaurus NLM uses the Semantic Network and Lexical Tools to produce the Metathesaurus. Metathesaurus production involves: —  Processing the terms and codes using the Lexical Tools —  Grouping synonymous terms into concepts —  Categorizing concepts by semantic types from the Semantic Network —  Incorporating relationships and attributes provided by vocabularies —  Releasing the data in a common format They can be accessed separately or in any combination according to your needs. 7/30/15 82

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Luciano informs healthcare_2015 Nashville, TN USA July 30 2015

  • 1.
     Health Web Observatories: CreatingPreferable Health Outcomes through Health Web Science Joanne S. Luciano, PhD Predictive Medicine, Inc., Belmont, MA (predmed.com) Rensselaer Polytechnic Institute, Troy, NY 30 July 2015 INFORMS Healthcare Conference 2015 Nashville, Tennessee, USA 7/30/15 1
  • 2.
    PRESENTER Joanne S. Luciano Enable Healthand Wellbeing through Knowledge Technology BS, MS Computer Science PhD Cognitive and Neural Systems (Computational Neuroscience) Wang Labs Harvard Medical School MITRE Lotus Development Predictive Medicine, Inc. Rensselaer Polytechnic Institute GE Global Research Labs Interests Flying planes, rocks: climbing, balancing and photographing them Community BioPathways Consortium, BioPAX, W3C HCLSIG, Yosemite Project, FIBO Email: jluciano@rpi.edu jluciano@predmed.com Always open to exploring opportunities. 7/30/15 2
  • 3.
    Multidisciplinary International Team 7/30/15 3 GrantCumming, Medical Doctor, NHS Grampian, Honorary Professor, University of the Highlands and Islands, AB24 2ZN, Aberdeen, United Kingdom, grant.cumming@nhs.net Tara French,Research Fellow, Institute of Design Innovation, The Glasgow School of Art, Horizon Scotland, Digital Health Institute, Forres IV36 2AB, United Kingdom, tara.french@dhi-scotland.com Eva Kahana,Distinguished University Professor and The Pierce T. and Elizabeth D. Robson Professor of the Humanities, Case Western Reserve University, Mather Memorial Building 231B, Cleveland OH 44106, United States of America, eva.kahana@case.edu David Molik,Computational Developer, Cold Spring Harbor Laboratories, One Bungtown Road, Cold Spring Harbor NY 11724, United States of America, dmolik@cshl.edu
  • 4.
    Objectives —  Formulating Healthcarefor the 21st Century —  Are we where we should be? —  What’s missing? —  How do we use the Web? —  How can we use the Web? —  How do we know what will work? —  What are the tools, technologies, and resources needed? —  How do we evaluate effectiveness? 7/30/15 4
  • 5.
    Brendan Ashby Master’s Thesis(RPI) Actively SEEKING FUNDING Nightingale Research to Practice Timeline(earlier work: 10 years in Software Research & Development and Product Development) 20091993 World Congress on Neural Networks, July 11-15, 1993, Portland, Oregon SIG Mental Function and Dysfunction Sam Levin Jackie Samson, Mc Lean Hospital Depression Research 1996 1995 20081994 Patents Sold to Advanced Biological Laboratories Belgium Patents Offered at Ocean Tomo Auction Chicago, IL US Patent No. 6,317,73 Awarded US Patents No. 6,063,028 Awarded 2001 2000 PhD Thesis Proposal Approved Workshop Neural Modeling of Cognitive and Brain Disorders BioPAX ? Linked Data W3C HCLS BioDASH EPOS 2006 EMPWR Poster Presented ISMB 1997 PSB 1998 1997 2010 Rensselaer (RPI) 2011 2012 2013 U Pitt Greg Siegle Depression Collaboration Yuezhang Xiao Master’s Thesis (RPI) Failed to get Funding for Proactive Multimodal Depression Treatment Health Web Science 7/30/15 5 2014 2015 Is 15-20 years too long to get from research to practice?
  • 6.
    Healthcare Singularity and theage of Semantic Medicine http://research.microsoft.com/en-us/collaboration/fourthparadigm/4th_paradigm_book_part2_gillam.pdf 2,300 years after the first report of angina for the condition to be commonly taught in medical curricula, modern discoveries are being disseminated at an increasingly rapid pace. 7/30/15 6
  • 7.
    Healthcare Singularity and theage of Semantic Medicine http://research.microsoft.com/en-us/collaboration/fourthparadigm/ 4th_paradigm_book_part2_gillam.pdf Focusing on the last 150 years, the trend still appears to be linear, approaching the axis around 2025. 7/30/15 7
  • 8.
    Times have changed — Aging population (end of life costly) —  More people with chronic illnesses (increased cost) —  The end of the blockbuster era (decrease in revenues, increase in drug development cost) —  Need lower drug development cost —  Personalized Medicine (right treatment to the right patient at the right time) —  Improved patient response to treatment (Evidence Based) —  Web and Mobile —  The technology (ubiquitous, monitor) —  Patient engagement increasing 8 Photos: http://www.flickr.com/photos/sepblog/4014143391/ http://allthingsd.com/files/2013/07/photo-12.jpg 7/30/15
  • 9.
    Data Driven Medicine: 3Shifts in thinking and practice: — Data, Not Programs (reuse!) — Sharing, Not Hoarding (or hiding) — Personal, Not (only) Population 9 7/30/15
  • 10.
  • 11.
    7/30/15 11 Health Web Observatories: CreatingPreferable Health Outcomes through Health Web Science
  • 12.
    7/30/15 12 The impact ofthe personal computer and internet on an individuals potential to influence society.
  • 13.
    7/30/15 13 Health Web Sciencerecognizes the revolutionary impact of the Internet, made possible through the Web, with the potential to change health behaviors and health care worldwide. This impact on changing the practice of medicine can be considered in three areas: power, experience and speed.
  • 14.
    7/30/15 14 Web Science (WS) WebScience is about investigating how human behavior co-constitutes the Web. People who impose regulations, engineer the Web, produce content, or even just click on links change the Web how other people will see it. Vice versa, what people see and do on the Web will change their behavior. Web Science is about understanding this cycle. SteffenStaab
  • 15.
    7/30/15 15 1/3 world’s populationuse the Web [1] 80% look for health information online [2] •  Studies impact of the Web on health and wellbeing •  Aims towards a preventative, participatory, personalized, and predictive (P4) model of healthcare. •  Posits P4 can be achieved by the leveraging of the Web’s data, resources and nature. •  Studies the evolving social, political, economic, policy health related questions that emerge as a result of the use of the Web. Health Web Science (HWS) [1] Miniwatts Marketing Group 2012 [2] California Healthcare Foundation, Fox, S. 2011
  • 16.
  • 17.
    7/30/15 17 The World WideWeb •  Directly influences conscious behavior (Kahneman, System 2) through imparting information •  Indirectly influences unconscious behavior (Kahneman, System 1) through social interactions •  “co-conscious” interactions are the emergent collective consciousness of the networ The Web and Human Behavior Influence Health Outcomes HWS seeks to understand the dynamics of these behavioral influences in order to support users in achieving better health outcomes
  • 18.
    7/30/15 18 Instruments for WebStudy – what works and what doesn’t, i.e. when to use technology, policy, transparency? •  Enable data to be found •  Make the metadata available for use by others •  Study the data in context using metadata •  Aggregation and presentation of observations enable a feedback mechanism for preferable futures. A health Web Observatory is a system that gathers and links to health data on the Web in order to answer questions about the Web, the users of the Web and the way that they affect each other within the context of healthcare. Health Web Observatory (HWO)
  • 19.
    How? Technologies Needed toenable Health Web Science and the vision for 21st Century Medicine It’s all about the meaning! — Semantic Enabling: Web Observatories — Semantic Interoperability: — Shared Meaning: Yosemite Project — Inference: Ontologies and OWL — Linked Data: RDF, HTTP, URIs as terms 7/30/15 19
  • 20.
  • 21.
    How? Technologies Needed toenable Health Web Science and the vision for 21st Century Medicine It’s all about the meaning! — Semantic Enabling: Web Observatories — Semantic Interoperability: — Shared Meaning: Yosemite Project — Inference: Ontologies and OWL — Linked Data: RDF, HTTP, URIs as terms 7/30/15 21
  • 22.
  • 23.
    Unified Medical LanguageSystem Knowledge Sources The UMLS has three tools, called the UMLS Knowledge Sources: —  Metathesaurus: Terms and codes from many vocabularies, including CPT®, ICD-10-CM, LOINC®, MeSH®, RxNorm, and SNOMED CT® —  Semantic Network: Broad categories (semantic types) and their relationships (semantic relations) —  SPECIALIST Lexicon and Lexical Tools: Natural language processing tools 7/30/15 23
  • 24.
  • 25.
  • 26.
  • 27.
    How? Technologies Needed toenable Health Web Science and the vision for 21st Century Medicine It’s all about the meaning! — Semantic Enabling: Web Observatories — Semantic Interoperability: — Shared Meaning: Yosemite Project — Inference: Ontologies and OWL — Linked Data: RDF, HTTP, URIs as terms 7/30/15 27
  • 28.
  • 29.
    Ontology Spectrum Reuse ofterminological resources for efficient ontological engineering in Life Sciences by  Jimeno-Yepes, Antonio;  Jiménez-Ruiz, Ernesto;  Berlanga-Llavori, Rafael;  Rebholz-Schuhmann, Dietrich Journal: BMC Bioinformatics  Vol.  10  Issue  Suppl 10 DOI: 10.1186/1471-2105-10-S10-S4 http://www.mkbergman.com/wp-content/themes/ai3v2/ images/2007Posts/070501d_SemanticSpectrum.png Existing formalisms Strong Semantics Weak Semantics 7/30/15 29
  • 30.
    Application vs. Reference Ontology ReferenceOntology —  Intended as an authoritative source —  True to the limits of what is known (this changes!) —  Used by others —  Application Ontology —  Built to support a particular application (use case) —  Reused rather than define terms —  Skeleton structure to support application —  Terms defined refine or create new concepts directly or through new classes based on inference http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf 7/30/15 30
  • 31.
    Healthcare and LifeScience Goal: a suite of orthogonal interoperable reference ontologies Barry Smith U Buffalo, NCBO From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 The Open Biological and Biomedical Ontologies http://www.obofoundry.org 7/30/15 31
  • 32.
    How? Technologies Needed toenable Health Web Science and the vision for 21st Century Medicine It’s all about the meaning! — Semantic Enabling: Web Observatories — Semantic Interoperability: — Shared Meaning: Yosemite Project — Inference: Ontologies and OWL — Linked Data: RDF, HTTP, URIs as terms 7/30/15 32
  • 33.
    The Open Biologicaland Biomedical Ontologies From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 http://www.obofoundry.org 7/30/15 33
  • 34.
    Translational Medicine Ontology Overview ofselected types, subtypes (overlap) and existential restrictions (arrows) in the Translational Medicine Ontology. 7/30/15 34The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside Luciano et al. Journal of Biomedical Semantics 2011, 2(Suppl 2):S1 http://www.jbiomedsem.com/content/2/S2/S1 Bridge the Gap Between “Bench and Bedside”
  • 35.
    Translational Medicine Knowledge BaseTranslational MedicineOntology with mappings to ontologies and terminologies listed in the NCBO BioPortal. The TMO provides a global schema for Indivo-based electronic health records (EHRs) and can be used with formalized criteria for Alzheimer’s Disease. The TMO maps types from Linking Open Data sources. 7/30/15 35
  • 36.
    Individuals, Not Populations 36 Photo:http://www.flickr.com/photos/sepblog/4014143391/ http://safety-code.org/ Quickly retrieve pharmacogenomic markers of patients when needed No central storage of data is necessary, giving patients full control over their personal health information. 7/30/15
  • 37.
  • 38.
  • 39.
    Conclusion Creating Preferable HealthOutcomes through Health Web Science —  Web Science —  Health Web Observatories as web tools —  Semantic Technologies —  Standards and Interoperability Web Observatories are VERY EARLY STAGE in HEALTH —  Health Web Sciences Needs your help! 7/30/15 39 https://www.baby-connect.com/images/baby2.gif https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcTFXOU0CsGM8pddeiadAbtTirgIv- _3KeaL_fhKIYYFAMPEOTy3
  • 40.
  • 41.
    What is UMLS? TheUMLS, or Unified Medical Language System Enables Interoperability between computer systems —  Files —  Software that brings together many health and biomedical —  vocabularies and standards You can use the UMLS to enhance or develop applications, such as electronic health records, classification tools, dictionaries and language translators. http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf http://www.nlm.nih.gov/research/umls/quickstart.html 7/30/15 41
  • 42.
    Unified Medical LanguageSystem Access to the UMLS The UMLS Terminology Services (UTS) provides three ways to access the UMLS: —  Web Browsers You can search the data through UTS applications: —  Metathesaurus Browser - Retrieve UMLS concept information, including CUIs, semantic types, and synonymous terms. —  Semantic Network Browser - View the names, definitions, and hierarchical structure of the Semantic Network. —  Local Installation download, customize and load into your database system, or browse your data using the MetamorphoSys RRF browser. —  Web Services APIs You can use NLM’s application programming interfaces (APIs) to query the UMLS data within your own application. 7/30/15 42
  • 43.
    Unified Medical LanguageSystem License Required —  Request a license (FREE) —  Sign up for a UMLS Terminology Services (UTS) account. —  UMLS licenses are issued only to individuals —  NLM is a member of the IHTSDO (owner of SNOMED CT), and there is no charge for SNOMED CT use in the United States and other member countries. Some uses of the UMLS may require additional agreements with individual terminology vendors. The UTS account allows you to browse, download, and query the UMLS. 7/30/15 43
  • 44.
    Unified Medical LanguageSystem Use UMLS to link health information, medical terms, drug names, and billing codes across different computer systems. Some examples: —  Linking terms and codes between doctor, pharmacy, and insurance company —  Patient care coordination among several departments within a hospital —  SNOMED, ICD-9, LOINC, RxNorm – clinical setting, more about this later in the next part of the tutorial The UMLS has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research. http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf 7/30/15 44
  • 45.
    Overview Introduction (10 minutes) 1. Background 1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout 2.  Examples: (40 minutes) 1.  Reference Ontology Examples 1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5) 2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10) 3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences 7/30/15 45
  • 46.
    Examples 3 Reference OntologyExamples — UMLS – High level across biomedicine — BioPAX – Mid level – biological pathways — Gene Ontology (“GO”) – Gene annotation 2 Application Ontology Example — Influenza Ontology — Translational Medicine Ontology 7/30/15 46
  • 47.
    The Open Biologicaland Biomedical Ontologies From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 http://www.obofoundry.org 7/30/15 47
  • 48.
    BioPAX Biological PAthway eXchange An abstractdata model for biological pathway integration Initiative arose from the community 487/30/15
  • 49.
    49 Metabolic PathwaysBioPAX Level 1 BiologicalPathways of the Cell BioPAX A series of chemical reactions, catalyzed by enzymes The products of one are the reactants of the next e.g. Conversion, Transport 7/30/15
  • 50.
    50 BioPAX Level 2 BioPAX Biological Pathwaysof the Cell Cells are complex systems whose physiology is governed by an intricate network of Molecular Interactions (MIs) of which a relevant subset are protein–protein interactions (PPIs). Molecular Interaction Networks http://www.estradalab.org/research/ 7/30/15
  • 51.
    51 BioPAX Biological Pathways ofthe Cell Molecular Interaction Networks http://www.estradalab.org/research/ Human Protein Interaction Network (PIN) 7/30/15 BioPAX Level 2
  • 52.
    Biological Pathways ofthe Cell Adapted from Cell Signalling Biology - Michael J. Berridge - www.cellsignallingbiology.org - 2012 and http://www.hartnell.edu/tutorials/biology/signaltransduction.html 52 Signaling Pathways BioPAX Level 3 BioPAX Signaling molecules trigger cellular responses. Molecules bind to the cell surface causing a cascade of activation Reactions A activates B activates C…. 7/30/15
  • 53.
    53 Gene Regulation BioPAX Biological Pathways ofthe Cell The modulation of any of the stages of gene expression that control: which genes are switched on and off when, how long, and how much Gene regulation may occur many stages: Transcription Post-transcriptional modification RNA transport Translation mRNA degradation Post-translational modifications among many others (more recently discovered!) http://www.biology-online.org/dictionary/Gene_regulation http://en.wikipedia.org/wiki/Regulation_of_gene_expression 7/30/15
  • 54.
    54 Metabolic Pathways Molecular Interaction Networks Signaling Pathways Gene Regulation BioPAX Level 1 BioPAX Level 2 BioPAX Level3 BioPAX Level 4 BioPAX What’s a pathway? Depends on who you ask! Biological Pathways of the Cell 7/30/15
  • 55.
    BioPAX Ontology 55 Level 1v1.0 (July 7th, 2004) parts how the parts are known to interact a set of interactions 7/30/15
  • 56.
    BioPAX Biochemical Reaction 56 phosphoglucose isomerase5.3.1.9 OWL (schema) Instances (Individuals) (data) 7/30/15
  • 57.
    Before BioPAX WithBioPAX Common “computable semantic” enables scientific discovery >200 DBs and tools Database Application User BioPAX - Simplify 7/30/15 57
  • 58.
    Examples 3 Reference OntologyExamples — UMLS – High level across biomedicine — BioPAX – Mid level – biological pathways — Gene Ontology (“GO”) – Gene annotation 2 Application Ontology Example — Influenza Ontology — Translational Medicine Ontology 7/30/15 58
  • 59.
    The Open Biologicaland Biomedical Ontologies From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 http://www.obofoundry.org 7/30/15 59
  • 60.
    Gene Ontology (GO) Standard representations: — Gene and gene product attributes —  Across species and databases 7/30/15 60 [1] Rhee, S.Y, Wood, V., Dolinski, K. and Draghici, S. 2008. Use and misuse of the gene ontology annotations. Nature Reviews Genetics 9:509-515. [2] http://people.oregonstate.edu/~knausb/rna_seq/annot.pdf Structured controlled vocabularies organized as 3 independent Ontologies —  Molecular Interactions —  Biological Processes —  Cellular Location
  • 61.
    Gene Ontology Two KeyUses: —  Resource: to look up genes with similar functionality or location within the cell to help characterize the function of a sequence or structure —  Use to annotate genomes to enable the analysis of the genome through the annotation terms. 7/30/15 61
  • 62.
    Gene Ontology Evidence Codes Adaptedfrom: http://people.oregonstate.edu/~knausb/rna_seq/annot.pdf Rhee, S.Y, Wood, V., Dolinski, K. and Draghici, S. 2008. Use and misuse of the gene ontology annotations. Nature Reviews Genetics 9:509-515. See also: http://www.geneontology.org/GO.evidence.shtml Manually-assigned evidence codes fall into Four categories: Experimental Computational analysis Author statements, Curatorial statements 7/30/15 62 Inferred from Electronic Annotation (IEA) is not assigned by a curator.
  • 63.
    Sequence Ontology Sequence Ontology(SO) ‘terms and relationships used to describe the features and attributes of biological sequence.’ (E.g., binding_site, exon, etc.) SO http://www.sequenceontology.org/ sequence_attribute feature_attribute polymer_attribute sequence_location variant_quality sequence_feature junction region sequence_alteration sequence_variant functional_variant structural_variant Relationship (lots!) 7/30/15 63 (snuck this one in as another example)
  • 64.
    Overview Introduction (10 minutes) 1. Background 1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout 2.  Examples: (40 minutes) 1.  Reference Ontology Examples 1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5) 2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10) 3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences 7/30/15 64
  • 65.
    Examples 3 Reference OntologyExamples — UMLS – High level across biomedicine — BioPAX – Mid level – biological pathways — Gene Ontology (“GO”) – Gene annotation 2 Application Ontology Example — Influenza Ontology — Translational Medicine Ontology 7/30/15 65
  • 66.
    Application vs. Reference Ontology ReferenceOntology —  Intended as an authorative source —  True to the limits of what is known —  Used by others —  Application Ontology —  Built to support a particular application (use case) —  Reused rather than define terms —  Skeleton structure to support application —  Terms defined refine or create new concepts directly or through new classes based on inference http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf 7/30/15 66
  • 67.
  • 68.
  • 69.
    Overview Introduction (10 minutes) 1. Background 1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout 2.  Examples: (40 minutes) 1.  Reference Ontology Examples 1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5) 2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10) 3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences 7/30/15 69
  • 70.
    Examples 3 Reference OntologyExamples — UMLS – High level across biomedicine — BioPAX – Mid level – biological pathways — Gene Ontology (“GO”) – Gene annotation 2 Application Ontology Example — Influenza Ontology — Translational Medicine Ontology 7/30/15 70
  • 71.
    Overview Introduction (10 minutes) 1. Background 1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout 2.  Examples: (40 minutes) 1.  Reference Ontology Examples 1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5) 2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10) 3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences 7/30/15 71
  • 72.
    Best Practices Semantic WebMethodology & Technology Development Process Fox, Peter & McGuinness, Deborah 2008 http://tw.rpi.edu/web/doc/TWC_SemanticWebMethodology 7/30/15 72
  • 73.
    Generalized Ontology Evaluation Framework(GOEF) 73 Two stages: 1.  Recast use case into its components: Three Levels of Evaluation 2.  Evaluate components using objective metrics
  • 74.
    BioPortal http://bioportal.bioontology.org/ Provides access tocommonly used biomedical ontologies and to tools for working with them. BioPortal allows you to —  Browse —  the library of ontologies —  mappings between terms in different ontologies —  a selection of projects that use BioPortal resources —  Search —  biomedical resources for a term —  for a term across multiple ontologies —  Receive recommendations —  on which ontologies are most relevant for a corpus —  Annotate text —  with terms from ontologies All information available through the BioPortal Web site is also available through the NCBO Web service REST API. Please see REST API documentation for more information. http://www.bioontology.org/wiki/index.php/NCBO_REST_services 7/30/15 74
  • 75.
    Conferences 7/30/15 75 Conference on Semanticsin Health Care and Life Sciences (CSHALS) Semantic web applications and tools for life sciences (SWAT4LS) Edinburgh 2013
  • 76.
    Conclusion Tutorial sources —  BioPortal — W3C HCLSIG Consortia to join —  W3C HCLSIG —  OpenPHACTS —  Identifiers.org —  Pistoia Alliance —  BioPAX (check for new name) 7/30/15 76
  • 77.
    THANK YOU! RPI TetherlessWorld Constellation RPI Web Science Research Center Predictive Medicine, Inc. W3C Health Care & Life Science SIG BioPathways Consortium BioPAX Harvard Medical School, Mass General Hospital Abha Moitra, Petr Haug, Larry Hunter, Bob Powers, Scott Marshall, Matthias Samwald, Michel Dumontier, Ted Slater, Eric Neumann, Lynette Hirschman, Lynn Schriml, Rick Lathrop and many many others! NSF, NIH, NIST, IEEE and many others! 7/30/15 77
  • 78.
  • 79.
    HL-7 and RIM HL-7and RIM: http://www.w3.org/2013/HCLS-tutorials/ RIM/#%286%29 —  RDF RIM Tutorial Eric Prud'hommeaux, <eric@w3.org> —  Basic understanding of the structure of how data written in HL7's RIM can be expressed in RDF. —  It is not a substitute for HL7's documentation, but instead the author's notion of a quick way to familiarize oneself with the concepts and terms used in the RIM and how the graph structure of RDF is a natural way to represent this data. Copyright © 2013 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply. 7/30/15 79
  • 80.
    Personalized Medicine Components •  Understanddisease heterogeneity —  Comprehend disease progression •  Determine genetic and environmental contributors —  Create treatments against relevant targets —  drugs against relevant targets (molecular structures) —  Yoga against stress —  Exercise against obesity —  Elimination against food intolerance or allergy •  Develop markers to predict response •  Identify concrete endpoints to measure response 7/30/15 80
  • 81.
    Scope Ontology Uses —  KnowledgeManagement —  Annotate data (such as genomes) —  Access information (search, find, and access) —  Map across ontologies relate —  Data integration and exchange —  Model dynamic cellular processes —  Identify Drug Interactions —  Decision support —  SafetyCodes —  Diabetic Care —  Lab Alerts (Bodenreider YBMI 2008) http://themindwobbles.wordpress.com/2009/05/04/olivier-bodenreider-nlm- best-practices-pitfalls-and-positives-cbo-2009/ 7/30/15 81
  • 82.
    Unified Medical LanguageSystem Metathesaurus NLM uses the Semantic Network and Lexical Tools to produce the Metathesaurus. Metathesaurus production involves: —  Processing the terms and codes using the Lexical Tools —  Grouping synonymous terms into concepts —  Categorizing concepts by semantic types from the Semantic Network —  Incorporating relationships and attributes provided by vocabularies —  Releasing the data in a common format They can be accessed separately or in any combination according to your needs. 7/30/15 82