Semantic Approaches for Biomedical Knowledge Discovery

Michel Dumontier, Ph.D.
Associate Professor of Medicine (Biomedical Informatics)
Stanford University

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HTTP://XKCD.COM/242/@micheldumontier::DS:10‐10‐2014

Science3@micheldumontier::DS:10‐10‐2014

The unbelievable growth of scientific knowledge

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Thousands of databases curate the literature into consumable facts(problems: access, format, identifiers & linking)5@micheldumontier::DS:10‐10‐2014

Software is needed to analyze, predict and evaluate(problems: OS, versioning, input/output formats)6@micheldumontier::DS:10‐10‐2014

Ultimately, we develop fairly sophisticatedprograms/workflows to test our hypotheses

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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 scientificknowledge?
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So what do we need to achieve this?
1. Standards to construct and interrogate a
massive, decentralized network ofinterconnected data and software
2. Methods and Tools–––

To prepare, interlink, and query data
To mine and discover associations
To identify novel, supported associations

3. Incentives and penalties–

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Funding agencies, journals, institutions, societies,
conferences, workshops@micheldumontier::DS:10‐10‐2014

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

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we’re building a massive network of linked data

11 Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod‐cloud.net/"@micheldumontier::DS:10‐10‐2014

Bio2RDF is an open source project to unify therepresentation and interlinking of biological data using RDF.
Linked Data for the Life Scienceschemicals/drugs/formulations,genomes/genes/proteins, domains
Interactions, complexes & pathways
animal models and phenotypes
Disease, genetic markers, treatments
Terminologies & publications
12• Release 3 (June 2014): 11B+ interlinked
statements from 35 biomedical datasets• dataset description, provenance & statistics• Partnerships with EBI, NCBI, DBCLS, NCBO,
OpenPHACTS, and commercial tool providers@micheldumontier::DS:10‐10‐2014

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.
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drugbank_vocabulary:Drug
rdf:type
rdfs:label
drugbank:DB00586

Diclofenac [drugbank:DB00586]

drugbank_vocabulary:targets
drugbank_target:290

rdfs:label

Prostaglandin G/H synthase 2[drugbank_target:290]

rdf:type
drugbank_vocabulary:Target
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The linked data network expands
with every reference
DrugBank

drugbank_vocabulary:Drug
rdf:type
rdfs:label
drugbank:DB00586

diclofenac [drugbank:DB00586]

pharmgkb_vocabulary:xref
pharmgkb:PA449293rdfs:label

PharmGKB

diclofenac [pharmgkb:PA449293]

pharmgkb_vocabulary:Drug15@micheldumontier::DS:10‐10‐2014

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Bio2RDF offers a highly connected
network of data

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Graph summarization for 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)}
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You can use query assistants
http://sindicetech.com/sindice‐suite/sparqled/

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graph: http://sindicetech.com/analytics@micheldumontier::DS:10‐10‐2014

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> .
}}22@micheldumontier::DS:10‐10‐2014

Bio2RDF: 2M+ SPARQL queries per month23@micheldumontier::DS:10‐10‐2014

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

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multiple formalizations of the same kind ofdata do emerge, each with their own merit25@micheldumontier::DS:10‐10‐2014

Massive Proliferation of Ontologies / Vocabularies

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

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Semantic data integration, consistency checking andquery answering over Bio2RDF with the
Semanticscience Integrated Ontology (SIO)
omim:189931
uniprot:P05067

pharmgkb:PA30917

is ais aomim:Gene
uniprot:Protein

pharmgkb:Generefseq:Protein
dataset

is a

is a
is a
sio:gene

ontology
Knowledge Base
Querying Bio2RDF Linked Open Data with a Global Schema. Alison Callahan, José Cruz‐Toledo andMichel Dumontier. Bio‐ontologies 2012.
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SRIQ(D)10700+ axioms
1300+ classes
201 object properties (inc. inverses)
1 datatype property

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Bio2RDF and SIO powered SPARQL 1.1 federated query:
Find chemicals (from CTD) and proteins (from SGD) thatparticipate in the same process (from GOA)

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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::DS:10‐10‐2014

tactical formalization
Take what you needand represent it in a way that directly serves your objective

USER DRIVEN REPRESENTATIONSTANDARDS

identifying aberrant and pharmacological pathways

Biopax‐pathway exploration

predicting drug targets using organism phenotypes

FALDO‐powered genome navigation

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aberrant and pharmacological pathways
diseasegenepathwaydrug
Q1. Can we identify pathways that are associatedwith a particular disease or class of diseases?
Q2. Can we identify pathways are associated witha particular drug or class of drugs?
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Identification ofdrug and disease enriched pathways• Approach– Integrate 3 datasets• DrugBank, PharmGKB and CTD– Integrate 7 terminologies• MeSH, ATC, ChEBI, UMLS, SNOMED, ICD, DO– Formalize data of interest– Identify significant associations using enrichment
analysis over the fully inferred knowledge base

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Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics.
Bioinformatics. 2012.
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Formal knowledge
representation
as a strategy fordata integration

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Have you heard of OWL?

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Top Level Classes(disjointness)

pathway

drug

gene

diseaseReciprocal
Existentials

Class subsumption
mercaptopurine[pharmgkb:PA450379]

property chainsdrug
pathway

diseasegene

mercaptopurine[drugbank:DB01033]
Class Equivalencepurine‐6‐thiol[CHEBI:2208]
mercaptopurine[mesh:D015122]
mercaptopurine[ATC:L01BB02]

Formalized as an OWL‐EL ontology
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650,000+ classes, 3.2M subClassOf axioms, 75,000
equivalentClass axioms@micheldumontier::DS:10‐10‐2014

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 automatedreasoning.
• 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.

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Knowledge Discovery through Data
Integration and Enrichment Analysis

38•

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 thebinomial 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.
@micheldumontier::DS:10‐10‐2014

Tactical Formalization + Automated Reasoning
Offers Compelling Value for Certain Problems
We need to be smart about the goal, and how best toachieve it. Tactical formalization is another tool in thetoolbox.
We’ve formalized data as OWL ontologies to verify, fix andexploit 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 datao incorrect use of relationso incorrect assertion of identity (owl:sameAs)

Many other applications can be envisioned.
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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.
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animal models provide insight for on target effects• In the majority of 100 best selling drugs ($148B inUS alone), there is a direct correlation between
knockout phenotype and drug effect• 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.
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Identifying drug targets
from mouse knock‐out phenotypes
Main idea: if a drug’s phenotypes matches the phenotypes of anull model, this suggests that the drug is an inhibitor of the gene
phenotypes

similareffects

non‐functional
gene model

drug

ortholog

gene
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inhibits
human gene@micheldumontier::DS:10‐10‐2014

GENOTYPE
kcnj11c14/c14;
insrt143/+(AB)B6.Cg‐Alms1foz/fox/J

ALSM1(NM_015120.4)? + [‐][c.10775delC]

increased weight,
adipose tissue volume,
glucose homeostasis altered

increased food intake,hyperglycemia,
insulin resistanceobesity,diabetes mellitus,
insulin resistance

PHENOTYPE

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Terminological Interoperability(we must compare apples with apples)
Mouse
Phenotypes

PhenomeNet

Mammalian
Phenotype
Ontology

PhenomeDrug

Drug effects(mappings from UMLS to DO, NBO, MP)

Resting tremors

abnormal
motor function

sterotypic
behavior

REM disordersleep
disturbanceabnormal
EEG

Shuf ing gait

abnormal
locomotion

decreased
stride length

Unstable
postureabnormalcoordination

poor rotarod
performanceNeuronal loss in
Substantia Nigra

CNS neuron
degenerationax ondegeneration

Constipation

abnormaldigestive
physiology

decreased gut
peristalsis

Hyposmia

abnormalolfaction

failure to ndfood@micheldumontier::DS:10‐10‐2014

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 anddetermines the amount of information about a drug effectprofile D that is covered by a set of mouse model
phenotypes M.

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Loss of function models predicttargets 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@micheldumontier::DS:10‐10‐2014

Phenotype‐Based Drug Repurposing

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

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FDA Use Case:
TKI non‐QT Cardiotoxicity• 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, topredict/validate adverse events• What evidence could we gather to give credence that
TKI’s causes non‐QT cardiotoxicity?
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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

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HyQue• 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 thedata and how it should be interpreted 
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 (ESWC2012). Heraklion, Crete. May 27‐31, 2012.
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HyQue: A Semantic Web Application

Hypothesis

Evaluation

DataOntologies

Software@micheldumontier::DS:10‐10‐201452

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]

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Data retrieval is done with SPARQL

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Data Evaluation is done with SPIN
rules

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56@micheldumontier::DS:10‐10‐2014http://bio2rdf.org/drugbank:DB01268
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In Summary• This talk was about making sense and using
the structured data we already have
• RDF‐based Linked Open Data acts as a
substrate for query answering and task‐basedformalization in OWL• Discovery through the generation of testable
hypotheses in the target domain.
• Using Linked Data to evaluate scientifichypotheses
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Looking to the Future• Community guidelines for RDF‐based data anddataset descriptions (e.g. CEDAR)• Alignment and consolidation of OWL ontologies(e.g. UMLS)• Identifying and filling gaps in our knowledge (e.g.
Adam the Robot scientist)• Improving our coverage of available evidence(e.g. HyQue)• More sophisticated data mining (e.g. you!)62@micheldumontier::DS:10‐10‐2014

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)

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

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