Powering Scientific Discovery with the Semantic Web (VanBUG 2014)

Powering Scientific Discovery with the Semantic Web
1
Michel Dumontier, Ph.D.
Associate Professor of Medicine (Biomedical Informatics)
Stanford University
@micheldumontier::VanBUG:13-03-2014

2 @micheldumontier::VanBUG:13-03-2014HTTP://XKCD.COM/242/

Science

We use the biomedical literature to gather evidence
4 @micheldumontier::VanBUG:13-03-2014

we query literature-curated databases
(problems: access, format, identifiers & linking)

we use software to analyze, predict and evaluate
(problems: OS, versioning, input/output formats)

we develop fairly sophisticated workflows to
gather support for our hypotheses

Wouldn’t it be great if we could just find the evidence
required to support or dispute a scientific hypothesis using
the most up-to-date and relevant data, tools and scientific
knowledge?

madness!
1. Build a massive network of interconnected data
and software using web standards
– Bio2RDF + SADI
2. Generate and test scientific hypotheses
– prepare data (tactical formalization)
– discover associations (ontology-based enrichment)
– gather evidence (HyQue)
3. Contribute back to the global knowledge graph

The Semantic Web
is the new global web of knowledge
standards for publishing, sharing and querying
facts, expert knowledge and services
scalable approach for the discovery
of independently formulated
and distributed knowledge

Resource Description Framework
• It’s a language to represent knowledge
– Logic-based formalism -> automated reasoning
– graph-like properties -> data analysis
• Good for
– Describing in terms of type, attributes, relations
– Integrating data from different sources
– Sharing the data (W3C standard)
– Reusing what is available, developing what you need,
and contributing back to the web of data.

we’re building an incredible network of linked data
12 @micheldumontier::VanBUG:13-03-2014Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch

Linked Data for the Life Sciences
13
Bio2RDF is an open source project to unify the
representation and interlinking of biological data using RDF.
chemicals/drugs/formulations,
genomes/genes/proteins, domains
Interactions, complexes & pathways
animal models and phenotypes
Disease, genetic markers, treatments
Terminologies & publications
• Release 2 (Jan 2013): 1B+ interlinked statements
from 19 high value datasets
• dataset description, provenance & statistics
• Partnerships with EBI, NCBI, DBCLS, NCBO,
OpenPHACTS, and commercial tool providers

drugbank:DB00586
drugbank_vocabulary:Drug
rdf:type
drugbank_target:290
drugbank_vocabulary:Target
rdf:type
drugbank_vocabulary:targets
rdfs:label
Prostaglandin G/H synthase 2
[drugbank_target:290]
rdfs:label
Diclofenac [drugbank:DB00586]
14

The linked data network expands
with every reference
drugbank:DB00586
pharmgkb_vocabulary:Drug
rdf:type
rdfs:label
diclofenac [drugbank:DB00586]
pharmgkb:PA449293
drugbank_vocabulary:Drug
pharmgkb_vocabulary:xref
diclofenac [pharmgkb:PA449293]
rdfs:label
DrugBank
PharmGKB
15

Bio2RDF R2 connectivity

Graph summaries in query formulation
PREFIX drugbank_vocabulary: <http://bio2rdf.org/drugbank_vocabulary:>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?ddi ?d1name ?d2name
WHERE {
?ddi a drugbank_vocabulary:Drug-Drug-Interaction .
?d1 drugbank_vocabulary:ddi-interactor-in ?ddi .
?d1 rdfs:label ?d1name .
?d2 drugbank_vocabulary:ddi-interactor-in ?ddi .
?d2 rdfs:label ?d2name.
FILTER (?d1 != ?d2)
}
20

You can use the SPARQLed query
assistant with updated endpoints
http://sindicetech.com/sindice-suite/sparqled/
graph: http://sindicetech.com/analytics21

Use virtuoso’s built in faceted
browser to construct increasingly
complex queries with little effort

Federated Queries over Independent
SPARQL EndPoints
Get all protein catabolic processes (and more specific) in biomodels
SELECT ?go ?label count(distinct ?x)
WHERE {
service <http://bioportal.bio2rdf.org/sparql> {
?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> .
}
}

Bio2RDF: 1M+ SPARQL queries per month
UniProt: 6.4M queries per month
EBI: 3.5M queries (Oct 2013)

Despite all the data, it’s still hard to find answers to questions
Because there are many ways to represent the same data
and each dataset represents it differently

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

Massive Proliferation of Ontologies / Vocabularies

Multi-Stakeholder Efforts to Standardize
Representations are Reasonable,
Long Term Strategies for Data Integration

uniprot:P05067
uniprot:Protein
is a
sio:gene
is a is a
Semantic data integration, consistency checking and
query answering over Bio2RDF with the
Semanticscience Integrated Ontology (SIO)
dataset
ontology
Knowledge Base
pharmgkb:PA30917
refseq:Protein
is a
is a
omim:189931
omim:Gene pharmgkb:Gene
Querying Bio2RDF Linked Open Data with a Global Schema. Alison Callahan, José Cruz-Toledo and
Michel Dumontier. Bio-ontologies 2012.
29

SRIQ(D)
10700+ axioms
1300+ classes
201 object properties (inc. inverses)
1 datatype property

Bio2RDF and SIO powered SPARQL 1.1 federated query:
Find chemicals (from CTD) and proteins (from SGD) that
participate in the same process (from GOA)
SELECT ?chem, ?prot, ?proc
FROM <http://bio2rdf.org/ctd>
WHERE {
SERVICE <http://ctd.bio2rdf.org/sparql> {
?chemical a sio:chemical-entity.
?chemical rdfs:label ?chem.
?chemical sio:is-participant-in ?process.
?process rdfs:label ?proc.
FILTER regex (?process, "http://bio2rdf.org/go:")
}
SERVICE <http://sgd.bio2rdf.org/sparql> {
?protein a sio:protein .
?protein sio:is-participant-in ?process.
?protein rdfs:label ?prot .
}
} @micheldumontier::VanBUG:13-03-201431

tactical formalization
Take what you need
and represent it in a way that directly serves your objective
STANDARDSUSER DRIVEN REPRESENTATION
identifying aberrant and pharmacological pathways
predicting drug targets using organism phenotypes
Biopax-pathway exploration
SPARQL-powered genome navigation

aberrant and pharmacological pathways
Q1. Can we identify pathways that are associated
with a particular disease or class of diseases?
Q2. Can we identify pathways are associated with
a particular drug or class of drugs?
drug
pathway
disease

Identification of
drug and disease enriched pathways
• Approach
– Integrate 3 datasets
• DrugBank, PharmGKB and CTD
– Integrate 7 terminologies
• MeSH, ATC, ChEBI, UMLS, SNOMED, ICD, DO
– Formalize selected elements
– Identify significant associations using enrichment
analysis over the fully inferred knowledge base
Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics.
Bioinformatics. 2012. @micheldumontier::VanBUG:13-03-201434

Formal knowledge
representation
as a strategy for
data integration

Have you heard of OWL?

mercaptopurine
[pharmgkb:PA450379]
mercaptopurine
[drugbank:DB01033]
purine-6-thiol
[CHEBI:2208]
Class Equivalence
mercaptopurine
[ATC:L01BB02]
Top Level Classes
(disjointness)
drug diseasegenepathway
property chains
Class subsumption
mercaptopurine
[mesh:D015122]
Reciprocal
Existentials
drug disease
pathway gene
Formalized as an OWL-EL ontology
650,000+ classes, 3.2M subClassOf axioms, 75,000
equivalentClass axioms

Benefits: 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)
• ‘parasitic infectious disease’ (do:0001398) associated with 129
drugs, 15 more than are directly linked.

Knowledge Discovery through Data
Integration and Enrichment Analysis
• OntoFunc: Tool to discover significant associations between sets of objects
and ontology categories. enrichment of attribute among a selected set of
input items as compared to a reference set. hypergeometric or the
binomial distribution, Fisher's exact test, or a chi-square test.
• 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 for treating HIV/AIDS. Side
effects include fatigue, headache, myalgia, malaise and anorexia
• We found 13,826 pathway-chemical associations
– Clopidogrel (chebi:37941) associated with Endothelin signaling
pathway (pharmgkb:PA164728163). Endothelins are proteins that
constrict blood vessels and raise blood pressure. Clopidogrel inhibits
platelet aggregation and prolongs bleeding time.

Tactical Formalization + Automated Reasoning
Offers Compelling Value for Certain Problems
We need to be smart about the goal, and how best to
achieve it. Tactical formalization is another tool in the
toolbox.
We’ve formalized data as OWL ontologies to verify, fix and
exploit Linked Data through expressive OWL reasoning
• To identify mistakes in human curated knowledge
• To identify conflicting meaning in terms
• To identify mistakes in the representation of RDF data
o incorrect use of relations
o incorrect assertion of identity (owl:sameAs)
Many other applications can be envisioned.

PhenomeDrug
A computational approach to predict drug
targets, drug effects, and drug indications using
phenotypes
Mouse model phenotypes provide information about human drug targets.
Hoehndorf R, Hiebert T, Hardy NW, Schofield PN, Gkoutos GV, Dumontier M.
Bioinformatics. 2013.

animal models provide insight for on target effects
• In the majority of 100 best selling drugs ($148B in US
alone), there is a direct correlation between knockout
phenotype and drug effect
• Gastroesophageal reflux
– Proton pump inhibitors (e.g Prilosec - $5B)
– KO of alpha or beta unit of H/K ATPase are achlorhydric (normal
pH) in stomach compared to wild type (pH 3-5) when exposed
to acidic solution
• Immunological Indications
– Anti-histamines (Claritin, Allegra, Zyrtec)
– KO of histamine H1 receptor leads to decreased responsiveness
of immune system
– Predicts on target effects : drowsiness, reduced anxiety
Zambrowicz and Sands. Nat Rev Drug Disc. 2003.

Identifying drug targets
from mouse knock-out phenotypes
drug
gene
phenotypes effects
human gene
non-functional
gene model
ortholog
similar
inhibits
Main idea: if a drug’s phenotypes matches the phenotypes of a
null model, this suggests that the drug is an inhibitor of the gene

Terminological Interoperability
(we must compare apples with apples)
Mouse
Phenotypes
Drug effects
(mappings from UMLS to DO, NBO, MP)
Mammalian
Phenotype
OntologyPhenomeNet
PhenomeDrug

Semantic Similarity
Given a drug effect profile D and a mouse model M, we
compute the semantic similarity as an information weighted
Jaccard metric.
The similarity measure used is non-symmetrical and
determines the amount of information about a drug effect
profile D that is covered by a set of mouse model
phenotypes M.

Loss of function models predict
targets of inhibitor drugs
• 14,682 drugs; 7,255 mouse genotypes
• Validation against known and predicted inhibitor-target pairs
– 0.76 ROC AUC for human targets (DrugBank)
– 0.81 ROC AUC for mouse targets (STITCH)
• diclofenac (STITCH:000003032)
– NSAID used to treat pain, osteoarthritis and rheumatoid arthritis
– Drug effects include liver inflammation (hepatitis), swelling of liver
(hepatomegaly), redness of skin (erythema)
– 49% explained by PPARg knockout
• peroxisome proliferator activated receptor gamma (PPARg) regulates metabolism,
proliferation, inflammation and differentiation,
• Diclofenac is a known inhibitor
– 46% explained by COX-2 knockout
• Diclofenac is a known inhibitor

Using the Semantic Web to Gather
Evidence for Scientific Hypotheses
What evidence supports or disputes that TKIs are cardiotoxic?

• Tyrosine Kinase Inhibitors (TKI)
– Imatinib, Sorafenib, Sunitinib, Dasatinib, Nilotinib, Lapatinib
– Used to treat cancer
– Linked to cardiotoxicity.
• FDA launched drug safety program to detect toxicity
– Need to integrate data and ontologies (Abernethy, CPT 2011)
– Abernethy (2013) suggest using public data in genetics,
pharmacology, toxicology, systems biology, to
predict/validate adverse events
• What evidence could we gather to give credence that
TKI’s causes non-QT cardiotoxicity?
FDA Use Case:
TKI non-QT Cardiotoxicity

Jane P.F. Bai and Darrell R. Abernethy. Systems Pharmacology to Predict Drug Toxicity: Integration Across Levels of
Biological Organization. Annu. Rev. Pharmacol. Toxicol. 2013.53:451-473

• The goal of HyQue is retrieve and evaluate evidence
that supports/disputes a hypothesis
– hypotheses are described as a set of events
• e.g. binding, inhibition, phenotypic effect
– events are associated with types of evidence
• a query is written to retrieve data
• a weight is assigned to provide significance
• Hypotheses are written by people who seek answers
• data retrieval rules are written by people who know the
data and how it should be interpreted 
HyQue
1. HyQue: Evaluating hypotheses using Semantic Web technologies. J Biomed Semantics. 2011 May 17;2 Suppl 2:S3.
2. Evaluating scientific hypotheses using the SPARQL Inferencing Notation. Extended Semantic Web Conference (ESWC
2012). Heraklion, Crete. May 27-31, 2012.

HyQue: A Semantic Web Application
@micheldumontier::VanBUG:1
51
Software
OntologiesData
Hypothesis Evaluation

What evidence might we gather?
• clinical: Are there cardiotoxic effects associated with the drug?
– Literature (studies) [curated db]
– Product labels (studies) [r3:sider]
– Clinical trials (studies) [r3:clinicaltrials]
– Adverse event reports [r2:pharmgkb/onesides]
– Electronic health records (observations)
• pre-clinical associations:
– genotype-phenotype (null/disease models) [r2:mgi, r2:sgd; r3:wormbase]
– in vitro assays (IC50) [r3:chembl]
– drug targets [r2:drugbank; r2:ctd; r3:stitch]
– drug-gene expression [r3:gxa]
– pathways [r2:kegg; r3:reactome]
– Drug-pathway, disease-pathway enrichments [aberrant pathways]
– Chemical properties [r2:pubchem; r2.drugbank]
– Toxicology [r1.toxkb/cebs]

Data retrieval is done with SPARQL

Data Evaluation is done with SPIN
rules

http://bio2rdf.org/drugbank:DB01268

In Summary
• This talk was about making sense of the
structured data we already have
• RDF-based Linked Open Data acts as a substrate
for query answering and task-based formalization
in OWL
• Discovery through the generation of testable
hypotheses in the target domain.
• Using Linked Data to gather evidence regarding
scientific hypotheses
• The big challenge lies in establishing discovery
pipelines with user-generated scientific data.

Acknowledgements
Bio2RDF Release 2:
Allison Callahan, Jose Cruz-Toledo, Peter Ansell
Aberrant Pathways: Robert Hoehndorf, Georgios Gkoutos
PhenomeDrug: Tanya Hiebert, Robert Hoehndorf, Georgios
Gkoutos, Paul Schofield
TKI Cardiotoxicity: Alison Callahan, Tania Hiebert, Beatriz
Lujan, Sira Sarntivijai (FDA)

dumontierlab.com
michel.dumontier@stanford.edu
Website: http://dumontierlab.com
Presentations: http://slideshare.com/micheldumontier

Powering Scientific Discovery with the Semantic Web (VanBUG 2014)

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