Olivier Bodenreider Lister Hill National Center for Biomedical Communications Bethesda, Maryland - USA Experiences in visualizing and navigating biomedical ontologies and knowledge bases ISMB 2002 Fifth Annual Bio-Ontologies Meeting August 8, 2002

Lister Hill National Center for Biomedical Communications 2 Introduction 1 u Biomedical knowledge l Terminologies(names) l Ontologies(objects) l Knowledge bases(facts) u Common features l Terms / Concepts l Inter-concept relationships n Hierarchical n Associative

Lister Hill National Center for Biomedical Communications 3 Introduction 2 u Challenges l Volume of information n concepts n relationships l Orientation n Mapping to concepts n Visualizing concept spaces n Navigating concept spaces term knowledge

Lister Hill National Center for Biomedical Communications 4 Introduction 3 u SemNav l UMLS browser l Entry point: biomedical term l Display related concepts l Display properties of interconcept relationships l Allow navigation among concepts u GenNav l GO browser l Entry point: GO term or gene product name/symbol l Display related GO terms and gene products l Display properties of term/term and term/gene product relationships l Allow navigation between GO terms and gene products

Lister Hill National Center for Biomedical Communications 5 Outline u Background l Unified Medical Language System (UMLS) l Gene Ontology u Overview of the browsers l SemNav l GenNav u Common features u Differences

UMLS and GO

Lister Hill National Center for Biomedical Communications 7 Unified Medical Language System u Developed at NLM since 1990 u 13 th edition in 2002 u Integrates some 60 terminological resources l Clinical vocabularies (including specialties) l Core terminologies (anatomy, drugs, med. devices) l Administrative terminologies, standards u Integration l Synonymous terms are clustered in a concept l Hierarchies (trees) are combined in a graph structure

Lister Hill National Center for Biomedical Communications 8 Terminology integration Terms Duchenne muscular dystrophy MeSH, SNOMED CTV3, Jablonski, CRISP, DxPlain, MedDRA, LOINC pseudohypertrophic muscular dystrophy MeSH, CTV3 SNOMED X-liked recessive muscular dystrophy Jablonski Duchenne de Boulogne muscular dystrophy Jablonski Duchenne’s muscular dystrophy COSTAR severe generalized familial muscular dystrophy SNOMED Duchenne type progressive muscular dystrophy SNOMED

Lister Hill National Center for Biomedical Communications 9 Terminology integration Relationships UMLS Adrenal Cortex Diseases Hypoadrenalism Adrenal Gland Hypofunction Adrenal cortical hypofunction Adrenal Gland Diseases Addison’s Disease SNOMED MeSH AOD Read Codes

Lister Hill National Center for Biomedical Communications 10 UMLS u Two-level structure l Semantic Network n 134 Semantic Types (STs) n 54 types of relationships among STs l Metathesaurus n 800,000 concepts n ~10 M inter-concept relationships l Link = categorization Concept Metathesaurus Semantic Type Semantic Network categorization

Heart Concepts Metathesaurus Esophagus Left Phrenic Nerve Heart Valves Fetal Heart Medias- tinum Saccular Viscus Angina Pectoris Cardiotonic Agents Tissue Donors Anatomical Structure Fully Formed Anatomical Structure Embryonic Structure Body Part, Organ or Organ Component Pharmacologic Substance Disease or Syndrome Population Group Semantic Types Semantic Network

Lister Hill National Center for Biomedical Communications 12 Gene Ontology u Developed by the GO Consortium u Several components l Ontology (~11,000 concepts) n Molecular functions n Cellular components n Biological processes l Gene products (~125,000) l Associations between Gene products and GO concepts (~357,000)

SemNav

MeSH Browser

Lister Hill National Center for Biomedical Communications 18 SemNav Visualization options

Lister Hill National Center for Biomedical Communications 23 SemNav Relationships Dystrophin Concepts Semantic Types Muscular Dystrophy, Duchenne 55 Amino Acid, Peptide or Protein Disease or Syndrome Biologically Active Substance

GenNav

Material and Methods

Common features and differences

Lister Hill National Center for Biomedical Communications 31 Mapping query terms u Mapping terms to concepts l Matching criteria (exact, approximate) l Normalization techniques n work well on clinical terms n less applicable to gene names u Query disambiguation With semantic type in SemNav With semantic type in SemNav With species in GenNav With species in GenNav

Lister Hill National Center for Biomedical Communications 32 Visualization u Graph vs. Trees (Forest) l Multiple inheritance is better visualized by graphs than by trees l Off-the-shelf, freely available graph visualization packages are available (GraphViz) u Need to reduce complexity l Transitive reduction on complex graphs l Feature selection e.g., a given vocabulary in SemNav e.g., a given vocabulary in SemNav e.g., a given species in GenNav e.g., a given species in GenNav

Lister Hill National Center for Biomedical Communications 33 Navigation u Tool for exploration Navigation among concepts ( SemNav and GenNav ) Navigation among concepts ( SemNav and GenNav ) Navigation between two poles (Gene products and GO concepts in GenNav ) Navigation between two poles (Gene products and GO concepts in GenNav )  Self-contained ( SemNav ) or opened to external resources ( GenNav )

Conclusions

Lister Hill National Center for Biomedical Communications 35 Conclusions  Most of the lessons learned while developing SemNav (for browsing general biomedical knowledge) were applicable to GenNav (for browsing molecular biology knowledge) u The lexical techniques suitable for mapping text to clinical terminologies require adaptation to the specificity of molecular biology terminologies

Contact: Olivier Bodenreider Lister Hill National Center for Biomedical Communications Bethesda, Maryland - USA SemNav * ► Resources ► Semantic Navigator ( * free UMLS registration required) GenNav