W3C Semantic Web for Health Care and Life Sciences Interest Group

1

• W3C Semantic Web for Health Care and Life
  Sciences Interest Group

2
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 Prudhommeaux
• 101 formal participants, and mailing list of gt
  600
• Information about the group
• http//www.w3.org/2001/sw/hcls/
• http//esw.w3.org/topic/HCLSIG

3
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

4
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 groups work
  at government, industry, and academic events

5
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)

6
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
  Prudhommeaux, Susie Stephens, Andrew Su, Steven
  Larson, Huajun Chen, TN Bhat, Matthias Samwald,
  Erick Antezana, Rob Frost, Ward Blonde, Holger
  Stenzhorn, Don Doherty

7
BioRDF Looking for Targets for Alzheimers

• Signal transduction pathways are considered to
  be rich in druggable targets
• CA1 Pyramidal Neurons are known to be
  particularly damaged in Alzheimers disease
• Casting a wide net, can we find candidate genes
  known to be involved in signal transduction and
  active in Pyramidal Neurons?

8
BioRDF Integrating Heterogeneous Data
PDSPki
NeuronDB
Reactome
Gene Ontology
BAMS
Allen Brain Atlas
BrainPharm
Antibodies
Entrez Gene
MESH
Literature
PubChem
Mammalian Phenotype
SWAN
AlzGene
Homologene
9
BioRDF SPARQL Query
10
BioRDF Results Genes, Processes

• DRD1, 1812 adenylate cyclase activation
• ADRB2, 154 adenylate cyclase activation
• ADRB2, 154 arrestin mediated desensitization of
  G-protein coupled receptor protein signaling
  pathway
• DRD1IP, 50632 dopamine receptor signaling
  pathway
• DRD1, 1812 dopamine receptor, adenylate cyclase
  activating pathway
• DRD2, 1813 dopamine receptor, adenylate cyclase
  inhibiting pathway
• GRM7, 2917 G-protein coupled receptor protein
  signaling pathway
• GNG3, 2785 G-protein coupled receptor protein
  signaling pathway
• GNG12, 55970 G-protein coupled receptor protein
  signaling pathway
• DRD2, 1813 G-protein coupled receptor protein
  signaling pathway
• ADRB2, 154 G-protein coupled receptor protein
  signaling pathway
• CALM3, 808 G-protein coupled receptor protein
  signaling pathway
• HTR2A, 3356 G-protein coupled receptor protein
  signaling pathway
• DRD1, 1812 G-protein signaling, coupled to
  cyclic nucleotide second messenger
• SSTR5, 6755 G-protein signaling, coupled to
  cyclic nucleotide second messenger
• MTNR1A, 4543 G-protein signaling, coupled to
  cyclic nucleotide second messenger
• CNR2, 1269 G-protein signaling, coupled to
  cyclic nucleotide second messenger
• HTR6, 3362 G-protein signaling, coupled to
  cyclic nucleotide second messenger
• GRIK2, 2898 glutamate signaling pathway

Many of the genes are related to AD through gamma
secretase (presenilin) activity
11
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 Prudhommeaux,
  Matthias Samwald, Susie Stephens, Jun Zhao

12
Linked Data

• 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

13
Dereferencing URIs over the Web
rdftype
foafPerson
pdcygri
foafname
Richard Cyganiak
foafbased_near
dbpediaBerlin
skossubject
dbpediaHamburg
skossubject
dbpediaMeunchen
14
LODD Data Sets
15
The Linked Data Cloud
16
Translational Medicine Ontology
17
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

18
Roles within Translational Medicine
19
Translational Medicine Use Cases
20
Translational Medicine Ontology
21
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

22
Scientific Discourse Overview
23
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

24
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

25
Scientific Discourse SWANSIOC

• 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

26
Scientific Discourse Workshop
http//esw.w3.org/topic/HCLS/ISWC2009/Workshop
27
COI Task Force

• Task Lead Vipul Kashap
• Participants Eric Prudhommeaux, Helen Chen,
  Jyotishman Pathak, Rachel Richesson, Holger
  Stenzhorn

28
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?

29
Terminology Task Force

• Task Lead John Madden
• Participants Chimezie Ogbuji, Helen Chen, Holger
  Stenzhorn, Mary Kennedy, Xiashu Wang, Rob Frost,
  Jonathan Borden, Guoqian Jiang

30
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)

31
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

32
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