Transcript HCLSIG$$Chairs$HCLS_Sep09

W3C Semantic Web for Health Care
and Life Sciences Interest Group
Background of the HCLS IG
• Originally chartered in 2005
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Chairs: Eric Neumann and Tonya Hongsermeier
• Re-chartered in 2008
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Chairs: Scott Marshall and Susie Stephens
Team contact: Eric Prud’hommeaux
• 101 formal participants, and mailing list of > 600
• Information about the group
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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
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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
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Kei Cheung (Yale University)
• Clinical Observations Interoperability – patient recruitment in trials
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Vipul Kashyap (Cigna Healthcare)
• Linking Open Drug Data – aggregation of Web-based drug data
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Chris Bizer (Free University Berlin)
• Pharma Ontology – high level patient-centric ontology
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Christi Denney (Eli Lilly)
• Scientific Discourse – building communities through networking
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Tim Clark (Harvard University)
• Terminology – Semantic Web representation of existing resources
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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?
BioRDF: Integrating Heterogeneous Data
PDSPki
Gene
Ontology
NeuronDB
Reactome
BAMS
Antibodies
Entrez
Gene
Allen Brain
Atlas
MESH
Literature
Mammalian
Phenotype
SWAN
AlzGene
BrainPharm
Homologene
PubChem
BioRDF: SPARQL Query
BioRDF: Results: Genes, Processes
DRD1, 1812
ADRB2, 154
ADRB2, 154
DRD1IP, 50632
DRD1, 1812
DRD2, 1813
GRM7, 2917
GNG3, 2785
GNG12, 55970
DRD2, 1813
ADRB2, 154
CALM3, 808
HTR2A, 3356
DRD1, 1812
SSTR5, 6755
MTNR1A, 4543
CNR2, 1269
HTR6, 3362
GRIK2, 2898
GRIN1, 2902
GRIN2A, 2903
GRIN2B, 2904
ADAM10, 102
GRM7, 2917
LRP1, 4035
ADAM10, 102
ASCL1, 429
HTR2A, 3356
ADRB2, 154
PTPRG, 5793
EPHA4, 2043
NRTN, 4902
CTNND1, 1500
adenylate cyclase activation
adenylate cyclase activation
arrestin mediated desensitization of G-protein coupled receptor protein signaling pathway
dopamine receptor signaling pathway
dopamine receptor, adenylate cyclase activating pathway
dopamine receptor, adenylate cyclase inhibiting pathway
G-protein coupled receptor protein signaling pathway
G-protein coupled receptor protein signaling pathway
G-protein coupled receptor protein signaling pathway
G-protein coupled receptor protein signaling pathway
G-protein coupled receptor protein signaling pathway
G-protein coupled receptor protein signaling pathway
G-protein coupled receptor protein signaling pathway
G-protein signaling, coupled to cyclic nucleotide second messenger
G-protein signaling, coupled to cyclic nucleotide second messenger
G-protein signaling, coupled to cyclic nucleotide second messenger
G-protein signaling, coupled to cyclic nucleotide second messenger
G-protein signaling, coupled to cyclic nucleotide second messenger
glutamate signaling pathway
glutamate signaling pathway
glutamate signaling pathway
glutamate signaling pathway
integrin-mediated signaling pathway
negative regulation of adenylate cyclase activity
negative regulation of Wnt receptor signaling pathway
Notch receptor processing
Notch signaling pathway
serotonin receptor signaling pathway
transmembrane receptor protein tyrosine kinase activation (dimerization)
ransmembrane receptor protein tyrosine kinase signaling pathway
transmembrane receptor protein tyrosine kinase signaling pathway
transmembrane receptor protein tyrosine kinase signaling pathway
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
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Survey publicly available data sets about drugs
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Explore interesting questions from pharma, physicians and
patients that could be answered with Linked Data
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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
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
Linked Data
Browsers
Linked Data
Mashups
Search
Engines
Thing
Thing
Thing
Thing
Thing
Thing
Thing
Thing
Thing
Thing
typed
links
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typed
links
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typed
links
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typed
links
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Dereferencing URIs over the Web
rdf:type
foaf:Person
pd:cygri
foaf:name
3.405.259
Richard Cyganiak
foaf:based_near
dp:population
dbpedia:Berlin
skos:subject
skos:subject
dp:Cities_in_Germany
dbpedia:Hamburg
dbpedia:Meunchen
skos:subject
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
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Linked to
– key concepts, entities and knowledge
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Specified
– by ontologies
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Integrated with
– existing software tools
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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
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Represent activities and contributions of online communities
Integration with blogging, wiki and CMS software
Use of existing ontologies, e.g. FOAF, SKOS, DC
• SWAN
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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 subdomain (e.g. pharmacological classification)
Accomplishments
• Technical
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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
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Conference Presentations and Workshops:
– Bio-IT World, WWW, ISMB, ISWC, AMIA, Society for Neuroscience, C-SHALS, etc.
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Publications:
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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
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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