W3C Semantic Web for Health Care and Life Sciences Interest Group

Background of the HCLS IG

• Originally chartered in 2005• Chairs: Eric Neumann and Tonya Hongsermeier

• Re-chartered in 2008• Chairs: Scott Marshall and Susie Stephens• Team contact: Eric Prud’hommeaux

• 101 formal participants, and mailing list of > 600

• Information about the group• http://www.w3.org/2001/sw/hcls/• http://esw.w3.org/topic/HCLSIG

Mission of HCLS IG

The mission of HCLS is to develop, advocate for, and support the use of Semantic Web technologies for

• Biological science• Translational medicine• Health care

These domains stand to gain tremendous benefit by adoption of Semantic Web technologies, as they depend on the interoperability of information from many domains and processes for efficient decision support

Group Activities

• Document use cases to aid individuals in understanding the business and technical benefits of using Semantic Web technologies

• Document guidelines to accelerate the adoption of the technology

• Implement a selection of the use cases as proof-of-concept demonstrations

• Develop high-level vocabularies

• Disseminate information about the group’s work at government, industry, and academic events

Task Forces

• BioRDF – integrated neuroscience knowledge base• Kei Cheung (Yale University)

• Clinical Observations Interoperability – patient recruitment in trials• Vipul Kashyap (Cigna Healthcare)

• Linking Open Drug Data – aggregation of Web-based drug data • Chris Bizer (Free University Berlin)

• Pharma Ontology – high level patient-centric ontology• Christi Denney (Eli Lilly)

• Scientific Discourse – building communities through networking• Tim Clark (Harvard University)

• Terminology – Semantic Web representation of existing resources• John Madden (Duke University)

BioRDF: Answering Questions

Goals: Get answers to questions posed to a body of collective knowledge in an effective way

Knowledge used: Publicly available databases, and text mining

Strategy: Integrate knowledge using careful modeling, exploiting Semantic Web standards and technologies

Participants: Kei Cheung, Scott Marshall, Eric Prud’hommeaux, Susie Stephens, Andrew Su, Steven Larson, Huajun Chen, TN Bhat, Matthias Samwald, Erick Antezana, Rob Frost, Ward Blonde, Holger Stenzhorn, Don Doherty

BioRDF: Looking for Targets for Alzheimer’s

• Signal transduction pathways are considered to be rich in “druggable” targets

• CA1 Pyramidal Neurons are known to be particularly damaged in Alzheimer’s disease

• Casting a wide net, can we find candidate genes known to be involved in signal transduction and active in Pyramidal Neurons?

NeuronDB

BAMS

Literature

Homologene

SWAN

Entrez Gene

Gene Ontology

Mammalian Phenotype

PDSPki

BrainPharm

AlzGene

Antibodies

PubChem

MESH

Reactome

Allen Brain Atlas

BioRDF: Integrating Heterogeneous Data

BioRDF: SPARQL Query

BioRDF: Results: Genes, ProcessesDRD1, 1812 adenylate cyclase activationADRB2, 154 adenylate cyclase activationADRB2, 154 arrestin mediated desensitization of G-protein coupled receptor protein signaling pathwayDRD1IP, 50632 dopamine receptor signaling pathwayDRD1, 1812 dopamine receptor, adenylate cyclase activating pathwayDRD2, 1813 dopamine receptor, adenylate cyclase inhibiting pathwayGRM7, 2917 G-protein coupled receptor protein signaling pathwayGNG3, 2785 G-protein coupled receptor protein signaling pathwayGNG12, 55970 G-protein coupled receptor protein signaling pathwayDRD2, 1813 G-protein coupled receptor protein signaling pathwayADRB2, 154 G-protein coupled receptor protein signaling pathwayCALM3, 808 G-protein coupled receptor protein signaling pathwayHTR2A, 3356 G-protein coupled receptor protein signaling pathwayDRD1, 1812 G-protein signaling, coupled to cyclic nucleotide second messengerSSTR5, 6755 G-protein signaling, coupled to cyclic nucleotide second messengerMTNR1A, 4543 G-protein signaling, coupled to cyclic nucleotide second messengerCNR2, 1269 G-protein signaling, coupled to cyclic nucleotide second messengerHTR6, 3362 G-protein signaling, coupled to cyclic nucleotide second messengerGRIK2, 2898 glutamate signaling pathwayGRIN1, 2902 glutamate signaling pathwayGRIN2A, 2903 glutamate signaling pathwayGRIN2B, 2904 glutamate signaling pathwayADAM10, 102 integrin-mediated signaling pathwayGRM7, 2917 negative regulation of adenylate cyclase activityLRP1, 4035 negative regulation of Wnt receptor signaling pathwayADAM10, 102 Notch receptor processingASCL1, 429 Notch signaling pathwayHTR2A, 3356 serotonin receptor signaling pathwayADRB2, 154 transmembrane receptor protein tyrosine kinase activation (dimerization)PTPRG, 5793 ransmembrane receptor protein tyrosine kinase signaling pathwayEPHA4, 2043 transmembrane receptor protein tyrosine kinase signaling pathwayNRTN, 4902 transmembrane receptor protein tyrosine kinase signaling pathwayCTNND1, 1500 Wnt receptor signaling pathway

Many of the genes are related to AD through gamma

secretase (presenilin) activity

Linking Open Drug Data

• HCLSIG task started October 1st, 2008• Primary Objectives

• Survey publicly available data sets about drugs• Explore interesting questions from pharma, physicians and

patients that could be answered with Linked Data

• Publish and interlink these data sets on the Web• Participants: Bosse Andersson, Chris Bizer, Kei Cheung, Don Doherty, Oktie Hassanzadeh, Anja Jentzsch, Scott Marshall, Eric Prud’hommeaux, Matthias Samwald, Susie Stephens, Jun Zhao

Linked Data

B C

Thing

typedlinks

A D E

typedlinks

typedlinks

typedlinks

Thing

Thing

Thing

Thing

Thing Thing

Thing

Thing

Thing

Search Engines

Linked DataMashups

Linked DataBrowsers

Use Semantic Web technologies to publish structured data on the Web and set links between data from one data source and data from another data sources

Dereferencing URIs over the Web

dp:Cities_in_Germany

3.405.259dp:population

skos:subject

Richard Cyganiak

dbpedia:Berlin

foaf:name

foaf:based_near

foaf:Personrdf:type

pd:cygri

skos:subject

skos:subject

dbpedia:Hamburg

dbpedia:Meunchen

LODD Data Sets

The Linked Data Cloud

Translational Medicine Ontology

Deliverables

• Review existing ontology landscape

• Identify scope of a translational medicine ontology through understanding employee roles

• Identify roughly 40 entities and relationships for template ontology

• Create 2-3 sketches of use cases (that cover multiple roles)

• Select and build out use case (including references to data sets)

• Build extensions to the ontology to meet the use case

• Build an application that utilizes the ontology

Roles within Translational Medicine

Translational Medicine Use Cases

Translational Medicine Ontology

Scientific Discourse Task Force

Task Lead: Tim Clark, John Breslin

Participants: Uldis Bojars, Paolo Ciccarese, Sudeshna Das, Ronan Fox, Tudor Groza, Christoph Lange, Matthias Samwald, Elizabeth Wu, Holger Stenzhorn, Marco Ocana, Kei Cheung, Alexandre Passant

Scientific Discourse: Overview

Scientific Discourse: Goals

• Provide a Semantic Web platform for scientific discourse in biomedicine

• Linked to– key concepts, entities and knowledge

• Specified– by ontologies

• Integrated with– existing software tools

• Useful to– Web communities of working scientists

Scientific Discourse: Some Parameters

• Discourse categories: research questions, scientific assertions or claims, hypotheses, comments and discussion, and evidence

• Biomedical categories: genes, proteins, antibodies, animal models, laboratory protocols, biological processes, reagents, disease classifications, user-generated tags, and bibliographic references

• Driving biological project: cross-application of discoveries, methods and reagents in stem cell, Alzheimer and Parkinson disease research

• Informatics use cases: interoperability of web-based research communities with (a) each other (b) key biomedical ontologies (c) algorithms for bibliographic annotation and text mining (d) key resources

Scientific Discourse: SWAN+SIOC

• SIOC• Represent activities and contributions of online communities• Integration with blogging, wiki and CMS software• Use of existing ontologies, e.g. FOAF, SKOS, DC

• SWAN• Represents scientific discourse (hypotheses, claims, evidence,

concepts, entities, citations)• Used to create the SWAN Alzheimer knowledge base• Active beta participation of 144 Alzheimer researchers• Ongoing integration into SCF Drupal toolkit

Scientific Discourse Workshop

http://esw.w3.org/topic/HCLS/ISWC2009/Workshop

COI Task Force

Task Lead: Vipul Kashap

Participants: Eric Prud’hommeaux, Helen Chen, Jyotishman Pathak, Rachel Richesson, Holger Stenzhorn

COI: Bridging Bench to Bedside

• How can existing Electronic Health Records (EHR) formats be reused for patient recruitment?

• Quasi standard formats for clinical data:• HL7/RIM/DCM – healthcare delivery systems • CDISC/SDTM – clinical trial systems

• How can we map across these formats?• Can we ask questions in one format when the data is represented in

another format?

Terminology Task Force

Task Lead: John Madden

Participants: Chimezie Ogbuji, Helen Chen, Holger Stenzhorn, Mary Kennedy, Xiashu Wang, Rob Frost, Jonathan Borden, Guoqian Jiang

Terminology: Overview

• Goal is to identify use cases and methods for extracting Semantic Web representations from existing, standard medical record terminologies, e.g. UMLS • Methods should be reproducible and, to the extent possible, not lossy• Identify and document issues along the way related to identification schemes, expressiveness of the relevant languages• Initial effort will start with SNOMED-CT and UMLS Semantic Networks and focus on a particular sub-domain (e.g. pharmacological classification)

Accomplishments

• Technical • HCLS KB hosted at 2 institutes, with content from over 20 data sources• Added many data sources to the Linked Data Cloud • Integration of SWAN and SIOC ontologies for Scientific Discourse• Demonstrator of querying inclusion/exclusion criterion across heterogeneous EHR systems

• Outreach• Conference Presentations and Workshops:

– Bio-IT World, WWW, ISMB, ISWC, AMIA, Society for Neuroscience, C-SHALS, etc.• Publications:

– iTriplification Challenge: Linking Open Drug Data– DILS: Linked Data for Connecting Traditional Chinese Medicine and Western Medicine– ICBO: Pharma Ontology: Creating a Patient-Centric Ontology for Translational Medicine– LOD Workshop, WWW: Enabling Tailored Therapeutics with Linked Data– AMIA Spring Symposium: Clinical Observations Interoperability: A Semantic Web Approach – W3C Note: Semantic Web Applications in Neuromedicine (SWAN) Ontology– W3C Note: SIOC, SIOC Types and Health care and Life Sciences– W3C Note: Alignment Between the SWAN and SIOC Ontologies– W3C Note: A Prototype Knowledge Base for the Life Sciences– W3C Note: Experiences with the Conversion of SenseLab Databases to RDF/OWL– BMC Bioinformatics: Advanced Translational Research with the Semantic Web

Conclusions

• Early access to use cases and best practice• Influence standard recommendations• Cost effective exploration of new technology through

collaboration• Network with others working on the Semantic Web• Group generates resources ranging from papers,

use cases, demos, ontologies, and data