Biohackathon 2012 keynote

1. Knowledge Discovery using an Integrated
Semantic Web
Michel Dumontier
Department of Biology, School of Computer Science, Institute of Biochemistry
Ottawa Institute for Systems Biology
Ottawa-Carleton Institute for Biomedical Engineering
Carleton University
Ottawa, Canada
Chair, W3C Semantic Web for Health Care and Life Sciences Interest Group
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2. 2 BH2012

3. 3 BH2012

4. uncovering a sufficient amount of evidence to support/refute
a hypothesis is becoming increasingly difficult
it requires a lot of digging around
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5. continuous growth in research literature
Source:http://www.nlm.nih.gov/bsd/stats/cit_added.html
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6. growing amount of biomedical data
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7. increasingly complex software & interfaces
to predict, compare and evaluate
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8. ultimately, we answer questions by building
sophisticated workflows
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9. What if we could just pose a hypothesis
and have a system automatically use
9
available data, ontologies and services?
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10. HyQue
HyQue is the Hypothesis query and evaluation system
• A platform for knowledge discovery
• Facilitates hypothesis formulation and evaluation
• Leverages Semantic Web technologies to provide access to
facts, expert knowledge and web services
• Conforms to a simplified event-based model
• Supports evaluation against positive and negative findings
• Transparent and reproducible evidence prioritization
• Provenance of across all elements of hypothesis testing
– trace a hypothesis to its evaluation, including the data and rules used
Evaluating scientific hypotheses using the SPARQL Inferencing Notation. Extended Semantic Web Conference
(ESWC 2012). Heraklion, Crete. May 27-31, 2012.
HyQue: evaluating hypotheses using Semantic Web technologies. J Biomed Semantics. 2011 May 17;2 Suppl 2:S3.
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11. HyQue Architecture
Ontologies
Services
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12. Event-based data model
HyQue events denote a phenomenon involving two
objects: ‘agent’ and ‘target’ . In addition, we can specify the
location of this event (e.g. located in nucleus, or under
some genetic background)
supported events
Event 1. protein-protein binding
‘has agent’ agent 2. protein-nucleic acid binding
‘has target’ target 3. molecular activation
‘is located in’ location
4. molecular inhibition
5. gene induction
‘is negated’ boolean
6. gene repression
7. transport
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13. HyQue domain rules CALCULATE a quantitative
measure of evidence for an event
‘induce’ rule (maximum score: 5):
– Is event negated? GO:0010628
• If yes, subtract 2
– Is event of type ‘induce’? CHEBI:36080
• If yes, add 1; if no, subtract 1
– Is agent of type ‘protein’ or ‘RNA’?
• If yes, add 1; if type ‘gene’, subtract 1
– Is target of type ‘gene’? SO:0000236
• If yes, add 1; if no, subtract 1
– Does agent have known ‘transcription factor activity’?
• If yes, add 1 GO:0003700
– Is event located in the ‘nucleus’?
• If yes, add 1; if no, subtract 1
GO:0005634
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14. Customization of rules/data sources will generate
different evidence-based evaluations
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15. The Semantic Web
is the new global web of knowledge
It involves standards for publishing, sharing and querying
facts, expert knowledge and services
It is a scalable approach to the
discovery of independently formulated
and distributed knowledge
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16. something you can search,
lookup, link to, check
consistency of, and query for
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17. An ever expanding web of linked data
17 “Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/” BH2012

18. Bio2RDF provides a
simple convention
and infrastructure to
provide linked data
for the life sciences
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19. linked data for the life sciences
An Open Source Project for the Provision of
Scalable, Decentralized Data with Global Mirroring
and Customizable Query Resolution
http://bio2rdf.org/ns:id Laval University, Carleton University,
Queensland University of Technology
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20. provides billions of interconnections
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21. Towards universally-accepted identifiers
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22. http://identifiers.org/taxonomy/$id
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23. (coming soon)
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24. engaging the BioPAX community to
adopt identifiers.org Andrea Splendiani
Pathwaycommons (level 2; download)
<bp:unificationXref rdf:ID="CPATH-LOCAL-653">
<bp:ID rdf:datatype="xsd:string">9606</bp:ID>
<bp:DB rdf:datatype="xsd:string">NCBI_TAXONOMY</bp:DB>
</bp:unificationXref>
Pathwaycommons (level 3; web service)
<bp:UnificationXref rdf:about="urn:biopax:UnificationXref:REACTOME+DATABASE+ID_109276">
<bp:id rdf:datatype = "http://www.w3.org/2001/XMLSchema#string">109276</bp:id>
<bp:db rdf:datatype = "http://www.w3.org/2001/XMLSchema#string">Reactome Database ID</bp:db>
</bp:UnificationXref>
Biomodels (level 3)
<bp:UnificationXref rdf:about="http://identifiers.org/obo.go/GO:0004889">
<bp:id rdf:datatype = "http://www.w3.org/2001/XMLSchema#string">GO:0004889</bp:id>
<bp:db rdf:datatype = "http://www.w3.org/2001/XMLSchema#string">Gene Ontology</bp:db>
</bp:UnificationXref>
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25. More sophisticated OWL-based Data Integration,
Consistency Checking and Discovery
Robert Hoehndorf
• Checking the consistency of semantic annotations [1]
– Formalized semantic annotations in SBML models as OWL axioms.
Automated reasoning uncovered inconsistencies in 16 models.
• e.g. alpha-D-glucose phosphate is not the required ATP in an ATP-dependent
reaction (GO + ChEBI + disjoint + closure axioms)
• Finding significant biomedical associations [2] (initiated at BH11)
– found significant associations between genes, drugs, diseases and
pathways using Drugbank, PharmGKB, CTD, PID across categories
of drugs (ChEBI, ATC, MeSH) and diseases (DO, MeSH)
– 22,653 pathway-disease type associations (6304 over; 16,349 under)
• carcinosarcoma (DOID:4236) and (HIV RT) Zidovudine Pathway
(PharmGKB:PA165859361)
– 13,826 pathway-chemical type associations (12,564 over; 1262 under)
• drug clopidogrel (CHEBI:37941) with Endothelin signaling pathway
(PharmGKB:PA164728163) -> (smooth muscle mitogenesis)
http://pharmgkb-owl.googlecode.com
1. Integrating systems biology models and biomedical ontologies. BMC Systems Biology. 2011. 5 : 124
2. Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics. Bioinformatics. 2012. in press
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26. Personal Health Lens
Mark Wilkinson
Chris Baker
Observation: Patients often look up new/alternative drugs to treat their
condition or alleviate side effects.
Opportunity: A patient-centric health care application that identifies
contraindications for drugs mentioned on web pages using the patient’s
own health data
Components:
• RDFized patient data
• Bio2RDF semantically annotated data
• SADI semantic web services to process the page and retrieve data
• SHARE automatic workflow composition
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28. Matthias Samwald
We are developing a simple, cheap and ubiquitous solutions for
anchoring pharmacogenomics in medical practice
Curated and unified
set of essential 385+
markers, 50+
pharmacogenes and
rulesystem unified
under one
standardized model:
The Medicine Safety
Code
W3C Task Force: Clinical Decision
Support for Personalized Medicine
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29. Unified
OWL
Ontology
(inferencing
,
consistency
checking,
mapping)
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30. At this Biohackathon
• refine Bio2RDF RDFization Guide
• complete Dataset Description (BH11)
• dataspace statistics & visualization
• SPARQL-based Enrichment Analysis
• ontology-based Similarity Networks
– see Rob’s email
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31. dumontierlab.com
michel_dumontier@carleton.ca
Website: http://dumontierlab.com
Presentations: http://slideshare.com/micheldumontier
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