HCLS Workshop @ ISWC Eric Neumann and Tonya Hongsermeier University of Georgia, Nov 6, 2006

W3C Semantic Web for HealthCare and Life Sciences Interest Group Launched Nov 2005: http://www.w3.org/2001/sw/hcls Co-chairs: Dr. Tonya Hongsermeier (Partners HealthCare); Eric Neumann (Teranode) Chartered to develop and support the use of SW technologies and practices to improve collaboration, research and development, and innovation adoption in the of Health Care and Life Science domains Based on a foundation of semantically rich specifications that support process and information interoperability HCLS Objectives: Core vocabularies and ontologies to support cross-community data integration and collaborative efforts Guidelines and Best Practices for Resource Identification to support integrity and version control Better integration of Scientific Publication with people, data, software, publications, and clinical trials

HCLS Philosophy Share use-cases, applications, demonstrations, experiences Expose collections as RDF using public tools Develop (where appropriate) core vocabularies for data integration

HCLS Activities BioRDF - data + NLP as RDF BioONT - ontology coordination Adaptive Clinical Protocols and Pathways Drug Safety and Efficacy Scientific Publishing - evidence management

Outline Basic Informatics Challenges Bench-to-Bedside Applications What is the Semantic Web? Current Activities… Case Studies

Drug Discovery and Medicine Health Practice Safety Prevention Privacy Knowledge Hygieia, G. Klimt

Large Data Sets Variables >> Samples Data Expansion Large Data Sets Variables >> Samples Many New Data Types Which Formats? Combine

Where Information Advances are Most Needed Supporting Innovative Applications in R&D Translational Medicine (Biomarkers) Molecular Mechanisms (Systems) Data Provenance, Rich Annotation Clinical Information eHealth Records, EDC, Clinical Submission Documents Safety Information, Pharmacovigilance, Adverse Events, Biomarker data Standards Central Data Sources Genomics, Diseases, Chemistry, Toxicology MetaData Ontologies Vocabularies

The Big Picture - Hard to understand from just a few Points of View

Complete view tells a very different Story

Distributed Nature of R&D Silos of Data…

Data Integration: Biology Requirements Disease Proteins Genes Papers Retention Policy Audit Trail Curation Tools Ontology Experiment Assays Compounds

New Regulatory Issues Confronting Pharmaceuticals Tox/Efficacy ADME Optim from Innovation or Stagnation, FDA Report March 2004

Translational Medicine in Drug R&D Early Middle Late Cellular Systems Human In Vitro Studies Animal Studies Clinical Studies Disease Models (Therapeutic Relevance) Toxicities Target/System Efficacy $500K $5M $500M

Translational Research Improve communication between basic and clinical science so that more therapeutic insights may be derived from new scientific ideas - and vice versa. Testing of theories emerging from preclinical experimentation on disease-affected human subjects. Information obtained from preliminary human experimentation can be used to refine our understanding of the biological principles underpinning the heterogeneity of human disease and polymorphism(s). http://www.translational-medicine.com/info/about Reference NIH Digital Roadmap activity

HCLS Framework: Biomedical Research Molecular, Cellular and Systems Biology/Physiology Organism as an integrated an interacting network of genes, proteins and biochemical reactions Human body as a system of interacting organs Molecular Cell Biology/Genomic and Proteomic Research Gene Sequencing, Genotyping, Protein Structures Cell Signaling and other Pathways Biomarker Research Discovery of genes and gene products that can be used to measure disease progression or impacts of drug Pharmaco-genomics Impact of genetic inheritance on Drug Discovery and Translational Research Use of preclinical research to identify promising drug candidates

HCLS Framework: Clinical Research Clinical Trials Determination of efficacy, impact and safety of drugs for particular diseases Pharmaco-vigilance/ADE Surveillance Monitoring of impacts of drugs on patients, especially safety and adverse event related information Patient Cohort Identification and Management Identifying patient cohorts for drug trials is a challenging task Translational Research Test theories emerging from pre-clinical experimentation on disease affected human subjects Development of EHRs/EMRs for both clinical research and practice Currently EHRs/EMRs focussed on clinical workflow processes Re-using that information for clinical research and trials is a challenging task

Ecosystem: Goal State /* Need to expand this with Biomedical Research + Clinical Practice */ Biomedical Research Clinial Practice /* Need to expand this to include Healthcare and Biomedical Research Players as well… Show an integrated picture with “continuous” information flow */

What is the Semantic Web ? It’s Text Extraction It’s AI It’s Semantic Webs It’s Web 2.0 It’s Data Tracking It’s Ontologies It’s a Global Conspiracy http://www.w3.org/2006/Talks/0125-hclsig-em/

The Current Web What the computer sees: “Dumb” links No semantics - <a href> treated just like <bold> Minimal machine-processable information

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

Understanding the Semantic Web Vision Some day in the future… Today-> describing data Core Concept: TRIPLES… Specifications RDF, OWL, GRDDL- Coming soon: SPARQL, RIF Applications Data Aggregation: Recombinant Data Statements: Annotating things Practices Everything gets a URI… New definition of Data Interoperability: DTA: Data Transit Authority Subject Object Property <Patient HB2122> <shows_sign> <Disease Pneumococcal_Meningitis>

Application Space : Semantic Web Drug DD Therapeutics safety Critical Path Chem Lib manufacturing NDA Production Genomics HTS Clinical Studies eADME Compound Opt Patent Biology DMPK genes informatics

URI - A key element Uniform Resource Identifier Specification used in HTML, XML, and RDF-OWL Fundamental to RDF: It IS the only valid SW identifier! Two forms: HTTP- http://biopax.org/pathway/kreb_cycle.owl URN- urn:lsid:biopax.org:pathway:kreb_cycle Resolution Mapping retrievable data to a URI Does not mean getting everything known about a URI Not clear how to best handle versioning See Alan’s slides…

REST-fulness REST is a term coined by Roy Fielding to describe an architecture style of networked systems. REST is an acronym standing for Representational State Transfer. http://www.molbio.org/gene (get gene list) http://www.molbio.org/gene/hugsk3b (get gene info) Can REST == URI, and if so, when? Yes, if we agree return function is identical to URI resolution Issues: Should it return RDF always? - standardized Resolution is only a subset of services, how do we handle non-resolution services: are these URI’s as well?

Opportunities for Semantics in HealthCare Enhanced interoperability via: Semantic Tagging Grounding of concepts in Standardized Vocabularies Complex Definitions Semantics-based Observation Capture Inference on Diseases Phenotypes Genetics Mechanisms Semantics-based Clinical Decision Support Guided Data Interpretation Guided Ordering Semantics-based Knowledge Management

Data Semantics in the Life Sciences Pathways, Biomarkers Publications Complex Objects with Categorical/Taxonomic Data Items Systems Biology Gene expression Publications + data Categorical Taxonomic Data Items Image + Text Data Items Data Items Text Text + data items Composite Objects with Embedded “process” Complex Objects Histology Profiling Glossary A collection of terms of interest with associated meanings Thesaurus A collection of terms organized in a hierarchical structure Database Schema A collection of table definitions representing concepts and relationships and column definitions representing properties. Use to describe a structured (typically relational) database RDF(S) W3C Standard called the Resource Description Framework (Schema) used to define and capture knowledge, typically richer than a database schema Ontylog Special kind of logical language based on description logics used to represent medical ontologies such as Snomed OWL W3C Standard called Web Ontology Language used to represent ontologies. Based on a family of description logics and has richer representational constructs when compared to Ontylog IEEE SUO An IEEE Working Group working to specify an upper ontology to support computer applications such as data interoperability, information search and retrieval, automated inferencing, and natural language processing. Consists of a wide variety of rich domain independent concepts Cyc Very well known effort to capture human common sense knowledge. Uses a rich representational language called Cyc-L which uses higher order logics to capture knowledge GO (Gene ontology). KEGG (Kyoto Encyclopedia of Genes and Genomes) is a bioinformatics resource for understanding higher order functional meanings and utilities of the cell or the organism from its genome information. TAMBIS (Transparent Access to Multiple Bioinformatics Information Source). TAMBIS aims to aid researchers in biological science by providing a single access point for biological information sources round the world. EcoCyc, a part of the BioCyc library, is a scientific database for the bacterium Escherichia coli. The EcoCyc project performs literature-based curation of the entire E. coli genome, and of E. coli transcriptional regulation, transporters, and metabolic pathways. BioPAX (Biological Pathways Exchange). genomics Clinical Findings Clinical trials Unstructured Data Types Structured and Complex Data Types

RDB => RDF Virtualized RDF

XML => RDF (GRDDL) XSL XML RDF GRDDL

RDFa: Bridging the Hypertext and Semantic Webs <div xmlns:cc="http://web.resource.org/cc/" xmlns:dc=”http://purl.org/dc/1.1/” about=”photo2.jpg”> This photo was taken by <span property=”dc:creator”>Ben Adida</span> and is licensed under a <a rel=”cc:license” href=”http://cc.org/licenses/by/2.5/”> Creative Commons License </a>. </div> photo2.jpg Ben Adida licenses/by/2.5/ dc:creator cc:license

Example: Knowledge Aggregation Courtesy of BG-Medicine

Case Study: Omics Subject  Verb  Object ApoA1 … … is produced by the Liver … is expressed less in Atherosclerotic Liver … is correlated with DKK1 … is cited regarding Tangier’s disease … has Tx Reg elements like HNFR1 Subject  Verb  Object

Knowledge Mining using Semantic Web “Gene Prioritization through Data Fusion” Aerts et al, 2006, Nature Use of quantitative and qualitative information for statistical ranking. Can be used to identify novel genes involved in diseases

Potential Linked Clinical Ontologies SNOMED CDISC Disease Descriptions Clinical Obs ICD10 Applications Clinical Trials ontology RCRIM (HL7) Disease Models Pathways (BioPAX) Mechanisms IRB Tox Genomics Molecules Extant ontologies Under development Bridge concept

Case Study: BioPAX (Pathways) <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> <bp:step-interactions> <bp:MODULATION rdf:ID="xDshToXGSK3b"> <bp:keft rdf:resource="#xDsh"/> <bp:right rdf:resource="#xGSK-3beta"/> <bp:participants rdf:resource="#xGSK-3beta"/> <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > <bp: participants rdf:resource="#xDsh"/> </bp: MODULATION > </bp: step-interactions > </bp: PATHWAYSTEP >

Case Study: BioPAX (Pathways) <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> <bp:step-interactions> <bp:MODULATION rdf:ID="xDshToXGSK3b"> <bp:keft rdf:resource="#xDsh"/> <bp:right rdf:resource="#xGSK-3beta"/> <bp:participants rdf:resource="#xGSK-3beta"/> <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > <drug:affectedBy rdf:resource=”http://pharma.com/cmpd/CHIR99102"/> <bp: participants rdf:resource="#xDsh"/> </bp: MODULATION > </bp: step-interactions > </bp: PATHWAYSTEP > Modulation CHIR99102 affectedBy

Case Study: Drug Discovery Dashboards Dashboards and Project Reports Next generation browsers for semantic information via Semantic Lenses Renders OWL-RDF, XML, and HTML documents Lenses act as information aggregators and logic style-sheets add { ls:TheraTopic hs:classView:TopicView }

Drug Discovery Dashboard http://www.w3.org/2005/04/swls/BioDash Topic: GSK3beta Topic Target: GSK3beta Disease: DiabetesT2 Alt Dis: Alzheimers Cmpd: SB44121 CE: DBP Team: GSK3 Team Person: John Related Set Path: WNT

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

Bridging Chemistry and Molecular Biology 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

Pathway 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 Non-synonymous polymorphisms from db-SNP

BioRDF Neuro Tasks Aggregate facts and models around Parkinson’s Disease BIRN / Human Brain Project SWAN: scientific annotations and evidence NeuroCommons Use RDF and OWL to describe ’Brain Connectivity' Neuronal data in SenseLab

BioRDF: Reagents RDF resources that describes various kinds of experimental reagents, starting with antibodies: Initial RDF that captures: Gene, the fact that this is an antibody, various kinds of pages about the antibody, such as vendor documentation, and any other properties that are explicitly captured in the source material Work with the Ontology task force to identify appropriate ontologies and vocabularies to use in the RDF. Write queries against the RDF to answer questions of the sort posed on the Alzforum's

BioRDF: NCBI NCBI Data: URIs and as RDF (Olivier Bodensreider) Terminology Integration: NLM’s UMLS, MESH SNOMED…

Conclusions: Key Semantic Web Principles Plan for change Free data from the application that created it Lower reliance on overly complex Middleware The value in "as needed" data integration Big wins come from many little ones The power of links - network effect Open-world, open solutions are cost effective Importance of "Partial Understanding"