Knowledge Discovery using an Integrated Semantic Web

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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 BH2012

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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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continuous growth in research literature

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Source:http://www.nlm.nih.gov/bsd/stats/cit_added.html

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growing amount of biomedical data

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increasingly complex software & interfaces to predict, compare and evaluate

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ultimately, we answer questions by building sophisticated workflows

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What if we could just pose a hypothesis and have a system automatically use

available data, ontologies and services? 9 BH2012

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

BH2012 10 HyQue: evaluating hypotheses using Semantic Web technologies. J Biomed Semantics. 2011 May 17;2 Suppl 2:S3.

Evaluating scientific hypotheses using the SPARQL Inferencing Notation. Extended Semantic Web Conference (ESWC 2012). Heraklion, Crete. May 27-31, 2012.

HyQue Architecture

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Services

Ontologies

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) Event

‘has agent’ agent

‘has target’ target

‘is located in’ location

‘is negated’ boolean

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supported events

1. protein-protein binding 2. protein-nucleic acid binding 3. molecular activation 4. molecular inhibition 5. gene induction 6. gene repression 7. transport

HyQue domain rules CALCULATE a quantitative measure of evidence for an event

‘induce’ rule (maximum score: 5): – Is event negated?

• If yes, subtract 2 – Is event of type ‘induce’?

• 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’?

• If yes, add 1; if no, subtract 1 – Does agent have known ‘transcription factor activity’?

• If yes, add 1 – Is event located in the ‘nucleus’?

• If yes, add 1; if no, subtract 1

GO:0010628

CHEBI:36080

SO:0000236

GO:0003700

GO:0005634

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Customization of rules/data sources will generate different evidence-based evaluations

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The Semantic Web is the new global web of knowledge

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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

something you can search, lookup, link to, check

consistency of, and query for

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An ever expanding web of linked data

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Bio2RDF provides a simple convention

and infrastructure to provide linked data for the life sciences

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linked data for the life sciences

An Open Source Project for the Provision of Scalable, Decentralized Data with Global Mirroring

and Customizable Query Resolution

Laval University, Carleton University, Queensland University of Technology

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http://bio2rdf.org/ns:id

provides billions of interconnections

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Towards universally-accepted identifiers

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http://identifiers.org/taxonomy/$id

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(coming soon)

engaging the BioPAX community to adopt identifiers.org

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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Andrea Splendiani

More sophisticated OWL-based Data Integration, Consistency Checking and Discovery

• 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)

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

http://pharmgkb-owl.googlecode.com

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Robert Hoehndorf

Personal Health Lens

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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Mark Wilkinson Chris Baker

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W3C Task Force: Clinical Decision Support for Personalized Medicine

Curated and unified set of essential 385+

markers, 50+ pharmacogenes and rulesystem unified

under one standardized model: The Medicine Safety

Code

We are developing a simple, cheap and ubiquitous solutions for anchoring pharmacogenomics in medical practice

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Matthias Samwald

Unified OWL

Ontology

(inferencing,

consistency checking, mapping)

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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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dumontierlab.com michel_dumontier@carleton.ca

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Website: http://dumontierlab.com Presentations: http://slideshare.com/micheldumontier

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