Generating Biomedical Hypotheses Using Semantic Web Technologies


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With its focus on investigating the nature and basis for the sustained existence of living systems, modern biology has always been a fertile, if not challenging, domain for formal knowledge representation and automated reasoning. Over the past 15 years, hundreds of projects have developed or leveraged ontologies for entity recognition and relation extraction, semantic annotation, data integration, query answering, consistency checking, association mining and other forms of knowledge discovery. In this talk, I will discuss our efforts to build a rich foundational network of ontology-annotated linked data, discover significant biological associations across these data using a set of partially overlapping ontologies, and identify new avenues for drug discovery by applying measures of semantic similarity over phenotypic descriptions. As the portfolio of Semantic Web technologies continue to mature in terms of functionality, scalability and an understanding of how to maximize their value, increasing numbers of biomedical researchers will be strategically poised to pursue increasingly sophisticated KR projects aimed at improving our overall understanding of the capability and behavior of biological systems.

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Generating Biomedical Hypotheses Using Semantic Web Technologies

  1. 1. Generating (and Testing) Biomedical Hypotheses Using Semantic Web Technologies Michel Dumontier, Ph.D. Associate Professor of Medicine (Biomedical Informatics) Stanford University 1 @micheldumontier::AAAI-FSW:Nov 16, 2013
  2. 2. 2 @micheldumontier::AAAI-FSW:Nov 16, 2013
  3. 3. Science, it works ****** 3 @micheldumontier::AAAI-FSW:Nov 16, 2013
  4. 4. We use the biomedical literature to find out what we believe to be true 4 @micheldumontier::AAAI-FSW:Nov 16, 2013
  5. 5. we need to access to the most recent biomedical data (problems: access, format, identifiers & linking) 5 @micheldumontier::AAAI-FSW:Nov 16, 2013
  6. 6. we also need access to the most effective software to analyze, predict and evaluate (problems: OS, versioning, input/output formats) 6 @micheldumontier::AAAI-FSW:Nov 16, 2013
  7. 7. we build support for our hypotheses by constructing and (partly) reusing fairly sophisticated workflows 7 @micheldumontier::AAAI-FSW:Nov 16, 2013
  8. 8. 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? 8 @micheldumontier::AAAI-FSW:Nov 16, 2013
  9. 9. madness! 1. Build a massive network of interconnected data and software using web standards 2. Develop methods to generate and test hypotheses across these data 1. tactical formalization 2. uncovering associations 3. evidence gathering 3. Contribute back to the global knowledge graph 9 @micheldumontier::AAAI-FSW:Nov 16, 2013
  10. 10. 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 10 @micheldumontier::AAAI-FSW:Nov 16, 2013
  11. 11. we’re building an incredible network of linked data 11 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch @micheldumontier::AAAI-FSW:Nov 16, 2013
  12. 12. Bio2RDF Linked Data Map Bio2RDF Release 2: Improved coverage, interoperability and provenance of Life Science Linked Data . ESWC 2013. 12 @micheldumontier::AAAI-FSW:Nov 16, 2013
  13. 13. At the heart of Linked Data for the Life Sciences chemicals/drugs/formulations, genomes/genes/proteins, domains Interactions, complexes & pathways animal models and phenotypes Disease, genetic markers, treatments Terminologies & publications • Free and open source • Uses Semantic Web standards • Release 2 (Jan 2013): 1B+ interlinked statements from 19 conventional and high value datasets • Includes provenance, statistics • Partnerships with EBI, NCBI, DBCLS, NCBO, OpenPHACTS, and commercial tool providers 13 A Callahan, J Cruz-Toledo, M Dumontier. Querying Bio2RDF Linked Open Data with a Global Schema. 2013. Journal of Biomedical Semantics. 4(Suppl 1):S1. @micheldumontier::AAAI-FSW:Nov 16, 2013
  14. 14. 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) – Reuse what is available, develop what you’re missing. 14 Dumontier::FDA:Nov 15,2013
  15. 15. 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 15 Dumontier::FDA:Nov 15,2013
  16. 16. 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:PA449293 rdfs:label PharmGKB diclofenac [pharmgkb:PA449293] pharmgkb_vocabulary:Drug 16 Dumontier::FDA:Nov 15,2013
  17. 17. Federated Queries over Independent SPARQL EndPoints Get all protein catabolic processes (and more specific) from biomodels SELECT ?go ?label count(distinct ?x) WHERE { service <> { ?go rdfs:label ?label . ?go rdfs:subClassOf ?tgo ?tgo rdfs:label ?tlabel . FILTER regex(?tlabel, "^protein catabolic process") } service <> { ?x <> ?go . ?x a <> . } } 17 @micheldumontier::AAAI-FSW:Nov 16, 2013
  18. 18. Bio2RDF: 1M+ SPARQL queries per month UniProt: 6.4M queries per month EBI: 3.5M queries (Oct 2013) 18 @micheldumontier::AAAI-FSW:Nov 16, 2013
  19. 19. Directions • research – approaches for data publication – methods for large scale data reduction – methods for mining associations • service – coverage, quality and licensing – better user interfaces – sustainability of service 19 @micheldumontier::AAAI-FSW:Nov 16, 2013
  20. 20. tactical formalization a. discovery of drug and disease pathway associations b. prediction of drug targets using phenotypes 20 @micheldumontier::AAAI-FSW:Nov 16, 2013
  21. 21. ontology as a strategy to formally represent and integrate knowledge 21 @micheldumontier::AAAI-FSW:Nov 16, 2013
  22. 22. Have you heard of OWL? 22 @micheldumontier::AAAI-FSW:Nov 16, 2013
  23. 23. Identification of disease enriched pathways def: A biological pathway is constituted by a set of molecular components that undertake some biological transformation to achieve a stated objective glycolysis : a pathway that converts glucose to pyruvate 23 @micheldumontier::AAAI-FSW:Nov 16, 2013
  24. 24. aberrant pathways which pathways are associated with disease? Which pathways can be perturbed by drugs? disease pathway drug 24 @micheldumontier::AAAI-FSW:Nov 16, 2013
  25. 25. 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 – Identify significant pathways via enrichment analysis over the fully inferred knowledge base • Formalized as an OWL-EL ontology – 650,000+ classes, 3.2M subClassOf axioms, 75,000 equivalentClass axioms 25 Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics. Bioinformatics. 2012. @micheldumontier::AAAI-FSW:Nov 16, 2013
  26. 26. Top Level Classes (disjointness) pathway drug gene disease Reciprocal Existentials Class subsumption mercaptopurine [pharmgkb:PA450379] purine-6-thiol [CHEBI:2208] Class Equivalence mercaptopurine [drugbank:DB01033] mercaptopurine [mesh:D015122] mercaptopurine [ATC:L01BB02] drug property chains pathway drug disease gene 26 @micheldumontier::AAAI-FSW:Nov 16, 2013
  27. 27. 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) retrieves 129 disease associated drugs, 15 more than are directly associated. 27 @micheldumontier::AAAI-FSW:Nov 16, 2013
  28. 28. 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. 28 @micheldumontier::AAAI-FSW:Nov 16, 2013
  29. 29. PhenomeDrug A computational approach to predict drug targets, drug effects, and drug indications using phenotypic information Mouse model phenotypes provide information about human drug targets. Hoehndorf R, Hiebert T, Hardy NW, Schofield PN, Gkoutos GV, Dumontier M. Bioinformatics. 2013. 29 @micheldumontier::AAAI-FSW:Nov 16, 2013
  30. 30. 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. 30 @micheldumontier::AAAI-FSW:Nov 16, 2013
  31. 31. Identifying drug targets from mouse knock-out phenotypes 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 phenotypes similar effects non-functional gene model drug ortholog gene 31 inhibits human gene @micheldumontier::AAAI-FSW:Nov 16, 2013
  32. 32. Terminological Interoperability (we must compare apples with apples) Mouse Phenotypes PhenomeNet Mammalian Phenotype Ontology PhenomeDrug Drug effects (mappings from UMLS to DO, NBO, MP) @micheldumontier::AAAI-FSW:Nov 16, 2013
  33. 33. 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. 33 @micheldumontier::AAAI-FSW:Nov 16, 2013
  34. 34. null models do well in predicting targets of drug inhibitors • 14,682 drugs; 7,255 mouse genotypes • Validation against known and predicted inhibitor-target pairs – 0.739 ROC AUC for human targets (DrugBank) – 0.736 ROC AUC for mouse targets (STITCH) – 0.705 ROC AUC for human 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::AAAI-FSW:Nov 16, 2013
  35. 35. Finding evidence to support or dispute a hypothesis takes some digging around 35 @micheldumontier::AAAI-FSW:Nov 16, 2013
  36. 36. 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, to predict/validate adverse events • What evidence could we gather to give credence that TKI’s causes non-QT cardiotoxicity? 36 @micheldumontier::AAAI-FSW:Nov 16, 2013
  37. 37. 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 the data 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 (ESWC 2012). Heraklion, Crete. May 27-31, 2012. 37 @micheldumontier::AAAI-FSW:Nov 16, 2013
  38. 38. HyQue: A Semantic Web Application Hypothesis Evaluation Data Ontologies Software @micheldumontier::AAAI- 38
  39. 39. SPIN functions to evaluate TKI Cardiotoxicity • Effects [r3.sider, lit] • Does the drug have known cardiotoxic effects? • Models [r2.mgi] • Are there cardiotoxic phenotypes in null mouse models of target genes? • Assays [ebi.chembl] • Is hERG inhibited? • Is the drug TUNEL positive? • Targets [r2.drugbank, lit] • Does the drug inhibit cardiotoxic-related proteins? • RAF1, PDFGFR, VEGFR, AMPK 39 @micheldumontier::AAAI-FSW:Nov 16, 2013
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  44. 44. 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. • The next big challenge lies in capturing the output of computational experiments just as much as extracting/formalizing user-contributed knowledge . 44 @micheldumontier::AAAI-FSW:Nov 16, 2013
  45. 45. 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 HyQue: Alison Callahan, Nigam Shah 45 @micheldumontier::AAAI-FSW:Nov 16, 2013
  46. 46. Website: Presentations: 46 @micheldumontier::AAAI-FSW:Nov 16, 2013