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Slide 1 :

1 Eric NeumannClinical Semantic Group W3C HCLS chair, MIT Fellow Tutorial: Semantic Web Applications in Clinical Data Management

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2 Tutorial Overview Bench-to-Bedside Vision Information Challenges Semantic Web: What is it? RDF: Recombinant Data (Aggregation) OWL: Vocabularies (NCI, SNOMED) Rules Translational Medicine Needs Clinical Data Standards- CDISC Re-Using Clinical Knowledge Retrospective DBs: JANUS Open Knowledge Benefits: Tox Commons

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3 Bench-to-Bedside Connecting pre-clinical and clinical studies Translational Medicine Patient Stratification & Personalized medicine (not the same) Knowledge and Data Integration Better Disease Understanding Next Generation Therapies, New Applications More Predictive (earlier) Safety Signals

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4 from Innovation or Stagnation, FDA Report March 2004

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5 New Regulatory Issues Confronting Pharmaceuticals from Innovation or Stagnation, FDA Report March 2004

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6 Translational Medicine Enable physicians to more effectively translate relevant findings and hypotheses into therapies for human health Support the blending of huge volumes of clinical research and phenotypic data with genomic research data Apply that knowledge to patients and finally make individualized, preventative medicine a reality for diseases that have a genetic basis

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7 Drug Discovery & Development Knowledge Qualified Targets Lead Generation Toxicity & Safety Biomarkers Pharmacogenomics Clinical Trials Molecular Mechanisms Lead Optimization Launch

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8 Ecosystem: Goal State Merging Biomed Research, Clinical Trials and Clinical Practice

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9 Drug R&D HCP Biomed Research Insurers Gov/Regulatory Public CROs Gov/Funding Grants Publications and Public Databases Disease Areas Chem Manuf Mol Path Res Clin Res BiomarkerTox Preclin Clin Safety Clin POC Surveillance Drug Programs Large Studies HMO,PPO Marketing VA System R&D BKB SafetyCommons Risks & Benefits JANUS HCChoices EHR HCLS Ecosystem

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10 Information Challenges No common way to bring data and docs together HTML links carries no meaning with them Today’s integration approaches prevent data re-use No global way to annotate our experiments and experiences Most annotations cannot be found by context No “sci-blog” for data interpretation Enterprise Information access and discoverability are weak Making timely discoveries! Why we all like Google Cutting and pasting between docs promotes fact mutation and loss of provenance Address business operations and tracking, and reduce static data copying

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11 A web of information Courtesy ofR. Stevens

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12 Diseases Assays Distributed Nature of Biomedical Knowledge Silos of Data… ClinicalTrials Targets Tox Patents Libraries Biomarkers Genotypes DrugRegistry HCS

Slide 13 :

13 The Big Picture In Drug R&D Hard to understand from just a few isolated Points of View

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14 What if Scientists could put it together for themselves?

Slide 15 :

15 Complete view tells a very different Story

Slide 16 :

16 Whose Schema?

Slide 17 :

17 Why Searching ala Google is not enough Google’s ability to rank and graph without using semantics is comparable to… … a Drug R&D Project that looks for associations, but makes no attempt to find or represent mechanisms of action

Slide 18 :

What is the Semantic Web?

Slide 19 :

19 The Layer Cake

Slide 20 :

20 The Current Web What the computer sees: “Dumb” links No semantics - treated just like Minimal machine-processable information

Slide 21 :

21 The Semantic Web Machine-processable semantic information Semantic context published – making the data more informative to both humans and machines

Slide 22 :

22 Needed to realize the SW vision A standard way of identifying things A standard way of describing things A standard way of linking things Standard vocabularies for talking about things

Slide 23 :

23 The Semantic WebBasic Standards for Describing Things Richer structure for basic resources (XML) Describe Data by Semantics and Not Syntax: RDF Define Semantics using RDFS or OWL Reference and Relate All Resources using URIs SPARQL is super model of SQL Rules for higher level reasoning

Slide 24 :

24 The Technologies: RDF Resource Description Framework (RDF) W3C standard for making statements or hypotheses about data and concepts Descriptive statements are expressed as triples: (Subject, Verb, Object) Subject Object Property

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25 Facts as triples PARK1 Parkinson disease has_associated_disease subject predicate object

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Slide 28 :

28 Semantic Web Technologies Richer structure for resources eXtensible Markup Language (XML) Exposed semantics Resource Description Framework (RDF) Explicit semantics Ontologies Web Ontology Language (OWL)

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29 The URI - global identification URI serves as a universal and uniform identifier for all web based resources.

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30 A Family of Identifiers URI = Uniform Resource Identifier URL = Uniform Resource Locator URN = Uniform Resource Name LSID = Life Science Identifier URI URL URN LSID URI = Uniform Resource Identifier URL = Uniform Resource Locator URN = Uniform Resource Name LSID = Life Science Identifier http://www.w3.org/Addressing/

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31 Uniform Resource Locator A type or resource identifier Identifies the location of a resource (or part thereof) Specifies a protocol to access the resource http, ftp, mailto E.g., http://www.nlm.nih.gov/ URI URL URN LSID

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32 Uniform Resource Name A type or resource identifier Identifies the name of a resource Location independent Defines a namespace E.g., urn:isbn:0-262-02591-4 urn:umls:C0001403 URI URL URN LSID

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Slide 34 :

34 RDF Examples …as RDF-XML …as N3 a cdisc:Subject ; nci:sex_code nci:Female ; cdisc:treatment ; cdisc:vitalSigns ; cdisc:adverseEvent .

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35 Semantic Data Integration: Incremental Roadmap Data assets remain as they are!They do not need to be modified The wrapper abstracts out details related to location, access and data structure Integration happens at the information level Highly configurable and incremental process Ability to specify declarative rules and mappings for further hypothesis generation

Slide 36 :

36 RDBM => RDF Virtualized RDF {primary keys} {primary keys}

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37 Semantic Data IntegrationBridging Clinical and Genomic Information “Paternal” 1 type degree Rule/Semantics-based Integration: Match Nodes with same Ids Create new links: IF a patient’s structured test result indicates a disease THEN add a “suffers from link” to that disease 90% evidence1

Slide 38 :

38 Semantic Data Integration:Bridging Clinical and Genomic Information RDF Graphs provide a semantics-rich substrate for decision support. Can be exploited by SWRL Rules

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Slide 40 :

40 Semantic Data Integration:Bridging Chemistry and Molecular Biology urn:lsid:uniprot.org:uniprot:P49841 Semantic Lenses: Different Views of the same data Apply Correspondence Rule:if ?target.xref.lsid == ?bpx:prot.xref.lsidthen ?target.correspondsTo.?bpx:prot BioPax Components Target Model

Slide 41 :

41 Lenses can aggregate, accentuate, or even analyze new result sets Behind the lens, the data can be persistently stored as RDF-OWL Correspondence does not need to mean “same descriptive object”, but may mean objects with identical references Semantic Data IntegrationBridging Chemistry and Molecular Biology

Slide 42 :

42 Semantic Data IntegrationPathway Polymorphisms Merge directly onto pathway graph Identify targets with lowest chance of genetic variance Predict parts of pathways with highest functional variability Map genetic influence to potential pathway elements Select mechanisms of action that are minimally impacted by polymorphisms

Slide 43 :

43 Scenario: Biomarker Qualification Semantics which Define… Biomarker Roles Disease Toxicity Efficacy Molecular and cytological markers Tissue-specific High content screening derived information Different sets associated with different predictive tools Statistical discrimination based on selected samples Predictive power Alternative cluster prediction algorithms Support qualifications from multiple studies (comparisons) Causal mechanisms Pathways Population variation

Slide 44 :

44 Semantic Data Integration: Advantages RDF: Graph based data model More expressive than the tree based XML Schema Model RDF: Reification Same piece of information can be given different values of belief by different clinical genomic researchers Potential for “Schema-less” Data Integration Hypothesis driven approach to defining mapping rules Can define mapping rules on the fly Incremental approach for Data Integration Ability to introduce new data sources into the mix incrementally at low cost Use of Ontology to disallow meaningless mapping rules? For e.g., mapping a gene to a protein…

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45 Semantic Data Integration“Schema-free” data integration Low cost approach for data integration No need for maintenance of costly schema mappings Ability to “merge” RDF graphs based on simple declarative rules that specify: Equality of URIs Connecting nodes of same type Connecting two nodes associated by a “path” Disadvantage: Potential for specifying spurious non-sensical rules

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46 Semantic Data IntegrationUse of Reification Level of accuracy of test result. Sensitivity and Specificity of lab result Level of confidence in genotyping or gene sequencing Probabilistic relationships Likelihood that a particular test result or condition is indicative of a disease or other medical condition Level of trust in a resource Results from a lab may be trusted more than result from another Results from well known health sites (NLM) may be trusted more than others Belief attribution Scientific hypotheses may be attributed to appropriate researchers

Slide 47 :

47 The Available Data Space Separate RDF documents are merged automatically into one aggregate graph.

Slide 48 :

48 Recombination in Molecular Genetics works due to proper alignment of genetic regions, thereby preventing gene loss, mangling, or duplication.

Slide 49 :

49 Recombinant Data Graphs can be filtered and pivoted, without losing meaning

Slide 50 :

50 Recombinant Data Mash-ups that don’t lose perspective Dynamic mixing of data Provide Different Views for Different Roles and Functions Dashboards Direct output of a SPARQL query

Slide 51 :

51 Key Functionality offered by Semantic Web Ubiquity Same identifiers for anything from anywhere Discoverability Global search on any entity Interoperability => “Recombinant Data” is Application Independence

Slide 52 :

52 Data Vision Aggregating data and statements using the Web Defined aggregation by need and role “Recombinant Data” Common system of referencing things (no copying) even is they exits in one of many databases Indexing things by types and with tags Common and ad hoc vocabularies Supporting the collective knowledge of an R&D Community A Wiki that has awareness about types and things New Generation Discovery Tools

Slide 53 :

53 Ontologies andWeb Ontology Language (OWL)

Slide 54 :

54 OWL Introduction History: DAML + OIL = OWL (2001) DAML – DARPA Agent Markup Language (1999) OIL – Ontology Inference Layer (1997) Based on RDF(S) Added features, mostly related to identity Restrictions Three flavors of increasing expressiveness, but decreasing tractability OWL Lite OWL DL (used for most applications) OWL Full

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Slide 56 : 56 OWL DL Example Class: Benign intracranial meningiomain the NCI Thesaurus Benign Intracranial Meningioma C5133 Benign Intracranial Meningioma Neoplastic Process Benign Intracranial Meningioma […] CL006955 http://cancer.gov/cancerinfo/terminologyresources/

Slide 57 :

57 OWL Class Constructors Borrowed from Tutorial on OWL by Bechhofer, Horrocks and Patel-Schneider http://www.cs.man.ac.uk/~horrocks/ISWC2003/Tutorial/

Slide 58 :

58 OWL Axioms Axioms (mostly) reducible to inclusion (v) C ´ D iff both C v D and D v C Borrowed from Tutorial on OWL by Bechhofer, Horrocks and Patel-Schneider http://www.cs.man.ac.uk/~horrocks/ISWC2003/Tutorial/

Slide 59 :

59 Existential vs. Universal Quantification Existential quantification owl:someValuesFrom Necessary condition E.g., migraine = headache & has_symptom throbbing pain [only if one-sided] Universal quantification owl:allValuesFrom Necessary and sufficient condition E.g., heart disease = disease & located_to heart

Slide 60 :

60 OWL reasoners For OWL DL, not OWL Full Reasoners Fact++ Pellet RacerPro Functions Consistency checking Automatic classification http://www.mindswap.org/2003/pellet/ http://www.racer-systems.com/ http://owl.man.ac.uk/factplusplus/

Slide 61 :

61 OWL Reasoners Details CEL Polynomial time classifier for the description logic EL+ EL+ is specially geared towards biomedical ontologies Cerebra Commerical C++ reasoner, Support for OWL-API Tableaux based reasoning for TBoxes and ABoxes Fact++ Free open source reasoner for DL reasoning Support for Lisp API and OWL API KAON2 Free Java based DL reasoner with support for SWRL fragment Support for DIG API MSPASS A generalized theorem prover for numerous logics, also works for DLs Pellet Free open source Java based reasoner for DLs Support for OWL, DIG APIs and Jena Interface RacerPro Commercial lisp based reasoner for DLs Support for OWL APIs and DIG APIs

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62 Editing OWL ontologies http://protege.stanford.edu/

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63 Resources available in OWL Many resources currently available in OWL Gene Ontology NCI Thesaurus Many projects using OWL e.g., BioPax NCBO - Mark Musen, Director http://www.geneontology.org/ http://cancer.gov/cancerinfo/terminologyresources/ http://www.biopax.org/

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64 OBO format Used to represent many ontologies in the OBO family (Open Biological Ontologies) Essentially a subset of OWL DL http://obo.sourceforge.net/ [Term] id: GO:0019563 name: glycerol catabolism namespace: biological_process def: "The chemical reactions and pathways resulting in the breakdown of glycerol … subset: gosubset_prok exact_synonym: "glycerol breakdown" [] exact_synonym: "glycerol degradation" [] xref_analog: MetaCyc:PWY0-381 is_a: GO:0006071 ! glycerol metabolism is_a: GO:0046174 ! polyol catabolism http://www.godatabase.org/dev/doc/obo_format_spec.html

Slide 65 :

65 Domain Semantics in Clinical Trials Clinical Semantics Patient/Subject ? Disease/Health state Diagnostics ? Findings Findings ? Inferred (proposed) Disease state Disease state ? Patient Classification / Segmentation Design ? Trial arms / treatments Observation ? POC, safety, mechanisms

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