tactical formalization of linked open data @micheldumontier::rda workshop:23-02-2015 1 michel...

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Tactical Formalization of Linked Open Data

@micheldumontier::RDA Workshop:23-02-2015

Michel Dumontier, Ph.D.

Associate Professor of Medicine (Biomedical Informatics)Stanford University


Linked Open Data provides an incredibly dynamic, rapidly growing set of interlinked resources


Linked Data for the Life Sciences

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Bio2RDF is an open source project to unify the representation and interlinking of biological data

chemicals/drugs/formulations, genomes/genes/proteins, domainsInteractions, complexes & pathwaysanimal models and phenotypesDisease, genetic markers, treatmentsTerminologies & publications

11B triples from 35 datasets

Each dataset is described using its own schema.

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Query data, ontologies, and services simultaneouslyusing Federated Queries over Independent SPARQL DBs

Get all protein catabolic processes (and more specific) in biomodels query against <http://bioportal.bio2rdf.org/sparql>

SELECT ?go ?label count(distinct ?x) WHERE { ?go rdfs:label ?label . ?go rdfs:subClassOf ?tgo ?tgo rdfs:label ?tlabel . FILTER regex(?tlabel, "^protein catabolic process") service <http://biomodels.bio2rdf.org/sparql> { ?x <http://bio2rdf.org/biopax_vocabulary:identical-to> ?go . ?x a <http://www.biopax.org/release/biopax-level3.owl#BiochemicalReaction> . }}


Gene Ontology

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Despite all the data, it’s still hard to find answers to questions

Because there are many ways to represent the same dataand each dataset represents it differently



multiple models for the same kind of data do emerge, each with their own merit


This lack of coordination makes Linked Open Data somewhat chaotic and unwieldy


Massive Proliferation of Ontologies / Vocabularies could be harnessed to bring order out of chaos

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The Semanticscience Integrated Ontology (SIO) Is used to ground Bio2RDF, SADI semantic web services

1300+ classes, 201 object properties (inc. inverses)1 datatype property

@micheldumontier::NIDM:Jan 26,201510

Ontology Design PatternsObjects, processes and their attributes.


Multi-Stakeholder Efforts to Standardize Representations are Reasonable,

Long Term Strategies for Data Integration

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tactical formalization


Turn linked data intowhatever you need

STANDARDSAPPLICATION SPECIFICDrug repurposingVerifying annotations in biomodelsDiscovering aberrant pathways

W3CCommunity-standardsApplication standardsVisualization standards

@micheldumontier::RDA Workshop:23-02-201513w/ Deborah McGuiness, Jim McCusker @ RPI


ReDrugS uses Nanopublications

• A nanopublication is a structured digital object to associate a statement composed of one or more triples with its evidence/provenance, and digital object metadata.

• http://nanopub.org


nanopublications are dynamically generated from a SPARQL query

Fit for purpose to a target schema

Here, we make an ontological commitment

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we can do more



Have you heard of OWL?

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SBML-based biomodels place semantic annotations in an annotation element

<species metaid="_525530" id="GLCi"

compartment="cyto" > <annotation> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:bqbiol="http://biomodels.net/biology-qualifiers/" xmlns:bqmodel="http://biomodels.net/model-qualifiers/">

<rdf:Description rdf:about="#_525530"> <bqbiol:is> <rdf:Bag> <rdf:li rdf:resource=“http://identifiers.org/obo.chebi/CHEBI:4167"/> <rdf:li rdf:resource=“http://identifiers.org/kegg.compound/C00031"/> </rdf:Bag> </bqbiol:is> </rdf:Description> </rdf:RDF> </annotation>

</species>

object

predicate(qualifier)

The intent is to express that the species represents a substance composed of glucose moleculesWe also know from the SBML model that this substance is located in the cytosol and with a (initial) concentration of 0.09765M

subject


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By converting models into formal representations of knowledge we get to:

o capture the semantics of models and the biological systems they represent

o leverage knowledge explicit in linked terminologieso validate the accuracy of the annotations / modelso discover biological implications inherent in the modelso query the results of simulations in the context of the

biological knowledge


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Model verification

After reasoning, we found 27 models to be inconsistent

reasons1. our representation - functions sometimes found in the place of

physical entities (e.g. entities that secrete insulin). better to constrain with appropriate relations

2. SBML abused – e.g. species used as a measure of time3. Incorrect annotations - constraints in the ontologies themselves

mean that the annotation is simply not possible


Integrating systems biology models and biomedical ontologies.BMC Syst Biol. 2011 Aug 11;5:124. doi: 10.1186/1752-0509-5-124.

http://www.ncbi.nlm.nih.gov/pubmed/21835028


Identification of drug and disease enriched pathways

• Approach– Integrated 3 datasets & 7 terminologies

• DrugBank, PharmGKB and CTD• MeSH, ATC, ChEBI, UMLS, SNOMED, ICD, DO

– Formalized into an OWL-EL ontology• 4 class top level ontology• logical class axioms• 650,000+ classes, 3.2M subClassOf axioms

– Identified significant associations using enrichment analysis over the fully inferred knowledge base

Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics. Bioinformatics. 2012.


Benefit 1: Enhanced Query Capability

– Use any mapped terminology to query a target resource.– Use knowledge in target ontologies to formulate more

precise questions• ask for drugs that are associated with diseases of the joint:

‘Chikungunya’ (do:0050012) is defined as a viral infectious disease located in the ‘joint’ (fma:7490) and caused by a ‘Chikungunya virus’ (taxon:37124).

– Learn relationships that are inferred by automated reasoning.

• alcohol (ChEBI:30879) is associated with alcoholism (PA443309) since alcoholism is directly associated with ethanol (CHEBI:16236)


Benefit 2: Knowledge Discovery through Enrichment Analysis

• OntoFunc: Tool to discover significant associations between sets of objects and ontology categories.

• We found 22,653 disease-pathway associations, where for each pathway we find genes that are linked to disease.– Mood disorder (do:3324) associated with Zidovudine

Pathway (pharmgkb:PA165859361). Zidovudine is used to treat HIV/AIDS. Side effects include fatigue, headache, myalgia, malaise and anorexia


http://tiny.cc/hcls-datadesc-ed

http://tiny.cc/hcls-datadesc-ed


61 metadata elementsCore• Identifiers• Title• Description• Attribution• Homepage• License• Language• Keywords• Concepts and vocabularies used• Standards• Publication

Editor & Validator underway

Provenance and Change• Version number• Source• Provenance: retrieved from, derived

from, created with• Frequency of change

Availability• Format• Download URL• Landing page• SPARQL endpoint

13 Content Statistics• With SPARQL queries


NIH Big Data to Knowledge (BD2K) Center of ExcellenceMark Musen (PI), Michel Dumontier (Co-I), Purvesh Khatri (Co-I), Olivier Gevaert (Co-I)Goals:• Tools to facilitate template construction and semi-automated metadata

annotation• Enable dataset discovery & ruse• Partnership with ImmPort imm. Repo, BioSharing, and the Stanford Library


[email protected]

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

http://dumontierlab.com/

http://slideshare.com/micheldumontier

tactical formalization of linked open data @micheldumontier::rda workshop:23-02-2015 1 michel...

Documents

rda workshop

query data

linked data intowhatever

data integration

kind of data

tgo rdfs

open source project

protein catabolic processes