EA 3888 – Conceptual Modeling of Biomedical Knowledge Faculty of Medicine - University of Rennes 1 Integrating and querying disease and pathway ontologies: building an OWL model and using RDFS queries Julie Chabalier, Olivier Dameron, Anita Burgun

EA 3888 – University of Rennes 1 Introduction Disease description in current medical ontologies Clinical features Etiology Location Morphology Example: SNOMED Clinical Terms® (SNOMED CT®) Disease Definitional manifestation causative agent finding site associated morphology

EA 3888 – University of Rennes 1 Introduction Characterization of diseases : biological knowledge required Genes ­ A gene mutation may result in a disease Metabolic pathways -A pathway may be shared by different phenotypes Biological processes -Different processes may explain different grades of a disease Biological knowledge Absent from medical ontologies

EA 3888 – University of Rennes 1 Objectives Integration of disease and pathway ontologies Ontology integration ­ Identify candidate ontologies ­ Get candidate ontologies in an adequate formalism ­ Integrate formalized ontologies Querying the resulting ontology ­ Consistency checking ­ Exploiting biomedical knowledge

EA 3888 – University of Rennes 1 Candidate ontologies KEGG Orthology (KO) hierarchy Organization of metabolic pathway and disease maps in the KEGG knowledge base DAG of four levels

EA 3888 – University of Rennes 1 Candidate ontologies ~ terms organized according to 3 hierarchies : - Molecular Function - Cellular Component - Biological Process Used to enrich the KO pathway definitions Gene Ontology (GO) the Gene Ontology

EA 3888 – University of Rennes 1 Candidate ontologies SNOMED-CT: clinical description of diseases Alzheimer's disease findingSite Intracranial glioma Brain structure Disorder of brain Dementia Cerebral structure Used to enrich the KO disease definitions findingSite Organic mental disorder Neoplasm of brain

EA 3888 – University of Rennes 1 Formalism OWL as a common formalism Unambiguous combination of several ontologies (URI, namespaces) Defined semantics Expressiveness (e.g disjointness) Getting candidate ontologies in OWL-DL KO: conversion of the 3 upper levels (available in text) GO: extraction of Biological Process hierarchy (available in OWL) SNOMED: extraction and conversion of the relevant concepts and relations (from UMLS)

EA 3888 – University of Rennes 1 Ontology integration Setting up relationships between ontologies Aligning: defining relationships between terms (is-a, part-of, etc.) Mapping: defining equivalence relationships between terms

EA 3888 – University of Rennes 1 Integration framework BioMed Ontology GO Biological Processes Disease and Pathway descriptions KO Pathways Diseases SNOMED Diseases Pathway descriptions Disease descriptions

EA 3888 – University of Rennes 1 Mapping GO processes – KO pathways GO biological processes KO Pathways Diseases Metamap program*: lexical mapping (labels and synonyms) KO: Metabolism KO: Carbohydrate metabolism GO: Metabolism GO: Macromolecule metabolism GO: Carbohydrate metabolism KO: Fructose and mannose metabolism SNOMED Diseases *Aronson, A.R. (2001) Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program, Proceedings of the AMIA Symp., 17-21

EA 3888 – University of Rennes 1 Aligning GO processes – KO pathways GO: Carbohydrate metabolism GO: Cellular carbohydrate metabolism GO: Monosaccharide metabolism GO: Hexose metabolism GO: Fructose metabolism GO: Mannose metabolism KO: Carbohydrate metabolism KO: Fructose and mannose metabolism GO: atomic conceptsKO: composite concepts Patterns to segment and recompose KO terms before the mapping KO: Fructose and mannose metabolism Fructose mannosemetabolism

EA 3888 – University of Rennes 1 Mapping & aligning GO processes – KO pathways GO: Carbohydrate metabolism GO: Cellular carbohydrate metabolism GO: Monosaccharide metabolism GO: Hexose metabolism GO: Fructose metabolism GO: Mannose metabolism KO: Carbohydrate metabolism KO: Fructose and mannose metabolism

EA 3888 – University of Rennes 1 Mapping of KO diseases and SNOMED diseases GO biological processes KO Pathways Diseases SNOMED Diseases Metamap program SN: Alzheimer's disease SN: Organic mental disorder SN: Dementia KO: Human diseases KO: Neurodegenerative disorders KO: Alzheimer's disease SN: Disorder of brain

EA 3888 – University of Rennes 1 Alignment of pathways and diseases GO Biological Processes KO Pathways Diseases Condition of alignment : if, at least, one gene is involved in both a disease D and a pathway P : 1 2 SNOMED Diseases Alignment: inferring relationships between : 1 - GO processes and KO diseases 2 - KO pathways and KO diseases D P hasPathway

EA 3888 – University of Rennes 1 1 Alignment of GO processes and KO diseases GO Biological Processes KO Pathways Diseases 2 SNOMED Diseases KEGG mapping (KEGG geneId - Uniprot id) GOA Genes Uniprot id GO id 1 hasPathway

EA 3888 – University of Rennes 1 GO Biological Processes KO Pathways Diseases 1 2 SNOMED Diseases Alignment of KO pathways and KO diseases KO: Metabolism KO: Carbohydrate metabolism KO: Glycolysis/Gluconeogenesis KO: gene1 KO: gene3 KO: Metabolism KO: Carbohydrate metabolism KO: Glycolysis/Gluconeogenesis KO: gene1 KO: gene3 KO: Human diseases KO: Neurodegenerative disorders KO: Alzheimer's disease KO: gene1 KO: gene2 KO: Human diseases KO: Neurodegenerative disorders KO: Alzheimer's disease KO: gene1 KO: gene2 hasPathway

EA 3888 – University of Rennes 1 Integration result BioMed Ontology classes: classes from GO 281 classes from KO pathways classes - 19 disease classes 146 classes from SNOMED

EA 3888 – University of Rennes 1 Integration results 144 KO pathways associated with GO processes (57%) 15 KO diseases associated with SNOMED Diseases (94%) 15 KO diseases associated with 836 distinct pathways (GO & KO) 3144 hasPathway relationships BioMed Ontology

EA 3888 – University of Rennes 1 Querying the BioMed Ontology Exploiting knowledge and checking consistency Taking into account the explicit relationships RDFS is sufficient RDF query language : SeRQL Implementation of SeRQL in Sesame is able to exploit RDFS semantics Exploitation of explicit relationships

EA 3888 – University of Rennes 1 SeRQL queries Example of an exploiting query Which pathways are shared by 2 neurological disorders : glioma & Alzheimers disease? SELECT DISTINCT Pathway, label(PathwayName) FROM {kpath:ko05010} rdfs:subClassOf {SuperClass}, {SuperClass} rdf:type {owl:Restriction}, {SuperClass} owl:onProperty {ea3888hp:hasPathway}, {SuperClass} owl:someValuesFrom {Pathway}, {Pathway} rdfs:label {PathwayName} INTERSECT SELECT DISTINCT Pathway, label(PathwayName) FROM {kpath:ko05214} rdfs:subClassOf {SuperClass}, {SuperClass} rdf:type {owl:Restriction}, {SuperClass} owl:onProperty {ea3888hp:hasPathway}, {SuperClass} owl:someValuesFrom {Pathway}, {Pathway} rdfs:label {PathwayName} SELECT DISTINCT Pathway, label(PathwayName) FROM {kpath:ko05010} rdfs:subClassOf {SuperClass}, {SuperClass} rdf:type {owl:Restriction}, {SuperClass} owl:onProperty {ea3888hp:hasPathway}, {SuperClass} owl:someValuesFrom {Pathway}, {Pathway} rdfs:label {PathwayName} INTERSECT SELECT DISTINCT Pathway, label(PathwayName) FROM {kpath:ko05214} rdfs:subClassOf {SuperClass}, {SuperClass} rdf:type {owl:Restriction}, {SuperClass} owl:onProperty {ea3888hp:hasPathway}, {SuperClass} owl:someValuesFrom {Pathway}, {Pathway} rdfs:label {PathwayName}

EA 3888 – University of Rennes 1 Query results Which pathways are shared by 2 neurological disorders : glioma & Alzheimers disease? 37 pathways: MAPK signaling pathway Focal adhesion Insulin signaling pathway Melanogenesis B cell receptor signaling pathway heart development central nervous system development axon guidance peptidyl-serine phosphorylation protein amino acid phosphorylation cell cycle cell-cell signaling cell cycle arrest lipid catabolic process lipid metabolic process ubiquitin cycle transport ErbB signaling pathway Wnt signaling pathway protein tetramerization intracellular signaling cascade protein modification process glycogen metabolic process anagen induction of apoptosis negative regulation of apoptosis apoptosis anti-apoptosis Natural killer cell mediated cytotoxicity cell proliferation DNA replication chromosome organization and biogenesis calcium ion homeostasis signal transduction response to UV negative regulation of cell growth cytoskeleton organization and biogenesis

EA 3888 – University of Rennes 1 hasPathway Query results By leveraging the pathway hierarchy: 66 pathways ( ) Alzheimers disease Intracellular protein transport Protein transport into nucleus, translocation Glioma hasPathway

EA 3888 – University of Rennes 1 Query results Example of a consistency query: Detect if a specific pathway and a more general one are associated with a same disease Disease1Pathway1 Pathway2 hasPathway Removal of redundant relationships

EA 3888 – University of Rennes 1 Conclusion Biomed Ontology project Integration Automatic method of integration of biomedical ontologies ­ Deals with the huge quantity of biomedical data ­ Takes into account the frequent updates of biomedical sources BioMed ontology ­ Integrates 3 biomedical ontologies (KO, GO, SNOMED) ­ Takes into account the formal evolution of the biomedical ontologies (OWL) Querying RDFS queries are enough: ­ to detect some basic inconsistencies of the BioMed ontology ­ to exploit the BioMed ontology

EA 3888 – University of Rennes 1 Perspectives Biological evaluation: study of glioma Increase the number of integrated biomedical sources (e.g. OMIM, BioPax) Improve the mapping/alignment techniques by taking into account the semantics in the patterns Associate a degree of confidence to the Disease/Pathway relationships (based for example on the GO evidence code)

EA 3888 – University of Rennes 1 BioMed ontology project :