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 Prud’hommeaux
Broad industry participation
Over 100 members
Mailing list of over 600
Background Information

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. Translating across domains
Translational medicine – use cases that cross domains
Link across domains and research:
What are the links?
gene – transcription factor – protein
pathway – molecular interaction – chemical compound
drug – drug side effect – chemical compound

5. Challenges
Support of legacy data(bases) Federated Query Interface (e.g. support for auto-completion, identifier lookup) Terminology and Ontology alignment Large scale reasoning (over large KB) Modeling hypothetical knowledge

6. Vision: Concept-based interfaces
The scientist should be able to work in terms of commonly used concepts. The scientist should be able to work in terms of personal concepts and hypotheses. - Not be forced to map concepts to the terms that have been chosen for a given application by the application builder.

7. Interface Sketch: Finding a basis for relation
Hypothesis
Epigenetic Mechanisms
“There is a relation”
Transcription
Chromatin
Transcription Factors
Histone Modification
Transcription Factor Binding Sites
Conceptual Biology
Drag a line between two concepts: results are like a query “Can the “influence checker” find a path through the instances?”
Classes
Instances
Common Domain
position

8. Biological cartoon as interface
Semantic approach to enable a systems approach, but start with two elements to integrate: Transcription factor binding sites and histones…
KSinBIT’06
Source: Marco Roos

9. 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

10. Current 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)

11. BioRDF Task Force Task Lead: Kei Cheung
Participants: M. 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

12. 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

13. 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?
Source: Alan Ruttenberg

14. Source: Susie Stephens
BioRDF: Integrating Heterogeneous Data
PDSPki
Gene Ontology
Reactome
NeuronDB
BAMS
Allen Brain Atlas
Antibodies
BrainPharm
Entrez Gene
MESH
Literature
PubChem
Mammalian Phenotype
SWAN
AlzGene
Homologene
Source: Susie Stephens

15. Source: Alan Ruttenberg
BioRDF: SPARQL Query
Source: Alan Ruttenberg

16. BioRDF: Results: Genes, Processes
DRD1, adenylate cyclase activation
ADRB2, 154 adenylate cyclase activation
ADRB2, 154 arrestin mediated desensitization of G-protein coupled receptor protein signaling pathway
DRD1IP, dopamine receptor signaling pathway
DRD1, dopamine receptor, adenylate cyclase activating pathway
DRD2, dopamine receptor, adenylate cyclase inhibiting pathway
GRM7, G-protein coupled receptor protein signaling pathway
GNG3, G-protein coupled receptor protein signaling pathway
GNG12, G-protein coupled receptor protein signaling pathway
DRD2, 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, G-protein coupled receptor protein signaling pathway
DRD1, G-protein signaling, coupled to cyclic nucleotide second messenger
SSTR5, G-protein signaling, coupled to cyclic nucleotide second messenger
MTNR1A, G-protein signaling, coupled to cyclic nucleotide second messenger
CNR2, G-protein signaling, coupled to cyclic nucleotide second messenger
HTR6, G-protein signaling, coupled to cyclic nucleotide second messenger
GRIK2, glutamate signaling pathway
GRIN1, glutamate signaling pathway
GRIN2A, glutamate signaling pathway
GRIN2B, glutamate signaling pathway
ADAM10, 102 integrin-mediated signaling pathway
GRM7, negative regulation of adenylate cyclase activity
LRP1, negative regulation of Wnt receptor signaling pathway
ADAM10, 102 Notch receptor processing
ASCL1, 429 Notch signaling pathway
HTR2A, serotonin receptor signaling pathway
ADRB2, 154 transmembrane receptor protein tyrosine kinase activation (dimerization)
PTPRG, ransmembrane receptor protein tyrosine kinase signaling pathway
EPHA4, transmembrane receptor protein tyrosine kinase signaling pathway
NRTN, transmembrane receptor protein tyrosine kinase signaling pathway
CTNND1, Wnt receptor signaling pathway
Many of the genes are related to AD through gamma secretase (presenilin) activity
Source: Alan Ruttenberg

17. 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

18. The Classic Web Single information space Built on URIs
Web Browsers
Search Engines
Single information space
Built on URIs
globally unique IDs
retrieval mechanism
Built on Hyperlinks
are the glue that holds everything together
HTML
HTML
HTML
hyper- links
hyper- links A B C
Source: Chris Bizer

19. 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 B C
Thing
typed links A D E
Search Engines
Linked Data Mashups
Linked Data
Browsers
Source: Chris Bizer

20. Data Objects Identified with HTTP URIs
rdf:type
pd:cygri
foaf:Person
foaf:name
Richard Cyganiak
foaf:based_near
dbpedia:Berlin
pd:cygri = dbpedia:Berlin =
Forms an RDF link between two data sources
Source: Chris Bizer

21. Dereferencing URIs over the Web
rdf:type
pd:cygri
foaf:Person
foaf:name
dp:Cities_in_Germany
dp:population
skos:subject
Richard Cyganiak
foaf:based_near
dbpedia:Berlin
Source: Chris Bizer

22. Dereferencing URIs over the Web
rdf:type
pd:cygri
foaf:Person
foaf:name
dp:Cities_in_Germany
dp:population
skos:subject
Richard Cyganiak
foaf:based_near
dbpedia:Berlin
skos:subject
dbpedia:Hamburg
skos:subject
dbpedia:Meunchen
Source: Chris Bizer

23. LODD Data Sets
Source: Anja Jentzsch

24. LODD in Marbles
Source: Anja Jentzsch

25. The Linked Data Cloud
Source: Chris Bizer

26. Pharma Ontology

27. Participants

28. Pharma Ontology Deliverables
Review existing ontology landscape
Identify scope of a pharma ontology through understanding employee roles
Identify roughly 30 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 relevant component of ontology for the use case
Build an application that utilizes the ontology

29. Existing Resources

30. Roles within Translational Medicine

31. 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

32. Scientific Discourse: Overview
Source: Tim Clark

33. 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
Source: Tim Clark

34. 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
Source: Tim Clark

35. Scientific Discourse: SWAN+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
Source: Tim Clark

36. COI Task Force Task Lead: Vipul Kashap
Participants: Eric Prud’hommeaux, Helen Chen, Jyotishman Pathak, Rachel Richesson, Holger Stenzhorn

37. 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?
Source: Holger Stenzhorn

38. Source: Eric Prud’hommeaux
COI: Use Case
Pharmaceutical companies pay a lot to test drugs
Pharmaceutical companies express protocol in CDISC
-- precipitous gap –
Hospitals exchange information in HL7/RIM
Hospitals have relational databases
Source: Eric Prud’hommeaux

39. Source: Holger Stenzhorn
Inclusion Criteria
Type 2 diabetes on diet and exercise therapy or
monotherapy with metformin, insulin
secretagogue, or alpha-glucosidase inhibitors, or
a low-dose combination of these at 50%
maximal dose. Dosing is stable for 8 weeks prior
to randomization. …
?patient takes metformin .
Source: Holger Stenzhorn

40. Source: Holger Stenzhorn
Exclusion Criteria
Use of warfarin (Coumadin), clopidogrel
(Plavix) or other anticoagulants. …
?patient doesNotTake anticoagulant .
Source: Holger Stenzhorn

41. Source: Holger Stenzhorn
Criteria in SPARQL
?medication1 sdtm:subject ?patient ; spl:activeIngredient ?ingredient1 .
?ingredient1 spl:classCode #metformin
OPTIONAL {
?medication2 sdtm:subject ?patient ; spl:activeIngredient ?ingredient2 . ?ingredient2 spl:classCode #anticoagulant
} FILTER (!BOUND(?medication2))
Source: Holger Stenzhorn

42. Terminology Task Force
Task Lead: John Madden
Participants: Chimezie Ogbuji, M. Scott Marshall, Helen Chen, Holger Stenzhorn, Mary Kennedy, Xiashu Wang, Rob Frost, Jonathan Borden, Guoqian Jiang

43. Features: the “bridge” to meaning
Concepts
Features
Data
Ontology
Keyword Vectors
Literature
Ontology
Image Features
Image(s)
The link to semantics is left to researchers
Ontology
Gene Expression Profile
Microarray
Ontology
Detected Features
Sensor Array

44. 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)

45. SKOS & the 80/20 principle: map “down”
Minimal assumptions about expressiveness of source terminology
No assumed formal semantics (no model theory)
Treat it as a knowledge “map”
Extract 80% of the utility without risk of falsifying intent
Why did we choose to go the SKOS route? Here are the reasons. We decided that safest and easiest route with the least risk was to “map down” to SKOS rather than to “map up” to RDFS or OWL. 45
Source: John Madden

46. E-science presentation Manchester September 25 2006
12/28/2017
Each set of components in the AIDA Toolbox is developed by a different expert in information retrieval, machine learning, and Semantic web. The motivating idea is that applications can be built by expert users such as bioinformaticians and food informaticians. Multidisciplinary collaboration is another important aspect of e-science.
The AIDA toolbox for knowledge extraction and knowledge management in a Virtual Laboratory for e-Science

47. SNOMED CT/SKOS under AIDA: retrieve
This is what it looks like, running semantically-guided free text search using SCT/SKOS. We can demo this.

50. Task Force Resources to federate
BioRDF – knowledge base, aTags (stored in KB)
Clinical Observations Interoperability – drug ontology
Linking Open Drug Data – LOD data
Pharma Ontology – ontology
Scientific Discourse – SWAN ontology, SWAN SKOS, myexperiment ontology
Terminology – SNOMED-CT, MeSH, UMLS

51. We’ve come a long way Triplestores have gone from millions to billions
Linked Open Data cloud
On demand Knowledge Bases: Amazon’s EC2
Terminologies: SNOMED-CT, MeSH, UMLS, ..
Neurocommons, Flyweb, Biogateway, Bio2RDF, Linked Life Data, ..

52. Accomplishments Technical Outreach HCLS KB hosted at 2 institutes
Linked Open Data contributions
Demonstrator of querying across heterogeneous EHR systems
Integration of SWAN and SIOC ontologies for Scientific Discourse
Outreach
Conference Presentations and Workshops:
Bio-IT World, WWW, ISMB, AMIA, C-SHALS, etc.
Publications:
Proceedings of LOD Workshop at WWW 2009: Enabling Tailored Therapeutics with Linked Data
Proceedings of the ICBO: Pharma Ontology: Creating a Patient-Centric Ontology for Translational Medicine
AMIA Spring Symposium: Clinical Observations Interoperability: A Semantic Web Approach
BMC Bioinformatics. A Journey to Semantic Web Query Federation in Life Sciences
Briefings in Bioinformatics.  Life sciences on the Semantic Web: The Neurocommons and Beyond

53. Someday, we should be able to find this as evidence for a fact in a Knowledge Base

54. Getting Involved Benefits to getting involved include: Get involved
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
Get involved
Speak to any of us after the session!
chairs and team contact
Participate in the next F2F (last one was here):