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

2. W3C Semantic Web for HealthCare and Life Sciences Interest Group
Launched Nov 2005:
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

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

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

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

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

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

8. 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

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

11. Complete view tells a very different Story

12. Distributed Nature of R&D
Silos of Data…

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

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

15. 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

16. 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).
Reference NIH Digital Roadmap activity

17. 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

18. 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

19. 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 */

20. 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

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

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

23. 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>

24. 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

25. 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-
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…

26. 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.
(get gene list)
(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?

27. 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

28. 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

29. RDB => RDF
Virtualized RDF

30. XML => RDF (GRDDL)
XSL
XML
RDF
GRDDL

31. RDFa: Bridging the Hypertext and Semantic Webs
<div xmlns:cc=" xmlns:dc=” 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=”
Creative Commons License
</a>.
</div>
photo2.jpg
Ben Adida
licenses/by/2.5/
dc:creator
cc:license

32. Example: Knowledge Aggregation
Courtesy of BG-Medicine

33. 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

34. 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

35. 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

36. 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=" Dishevelled to GSK3beta</bp:name>
<bp:direction rdf:datatype=" IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction >
<bp:control-type rdf:datatype=" INHIBITION</bp: control-type >
<bp: participants rdf:resource="#xDsh"/>
</bp: MODULATION >
</bp: step-interactions >
</bp: PATHWAYSTEP >

37. 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=" Dishevelled to GSK3beta</bp:name>
<bp:direction rdf:datatype=" IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction >
<bp:control-type rdf:datatype=" INHIBITION</bp: control-type >
<drug:affectedBy rdf:resource=”
<bp: participants rdf:resource="#xDsh"/>
</bp: MODULATION >
</bp: step-interactions >
</bp: PATHWAYSTEP >
Modulation
CHIR99102
affectedBy

38. 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 }

39. Drug Discovery Dashboard
Topic: GSK3beta Topic
Target: GSK3beta
Disease: DiabetesT2
Alt Dis: Alzheimers
Cmpd: SB44121
CE: DBP
Team: GSK3 Team
Person: John
Related Set
Path: WNT

40. 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

41. 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

42. 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

43. 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

44. 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

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

46. 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"