title: Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses.
abstract: BACKGROUND
  Semantic similarity analysis facilitates automated semantic explanations of biological and clinical data annotated by biomedical ontologies. Gene ontology (GO) has become one of the most important biomedical ontologies with a set of controlled vocabularies, providing rich semantic annotations for genes and molecular phenotypes for diseases. Current methods for measuring GO semantic similarities are limited to considering only the ancestor terms while neglecting the descendants. One can find many GO term pairs whose ancestors are identical but whose descendants are very different and vice versa. Moreover, the lower parts of GO trees are full of terms with more specific semantics.
  
  
  METHODS
  This study proposed a method of measuring semantic similarities between GO terms using the entire GO tree structure, including both the upper (ancestral) and the lower (descendant) parts. Comprehensive comparison studies were performed with well-known information content-based and graph structure-based semantic similarity measures with protein sequence similarities, gene expression-profile correlations, protein-protein interactions, and biological pathway analyses.
  
  
  CONCLUSIONS
  The proposed bidirectional measure of semantic similarity outperformed other graph-based and information content-based methods.
authors.affiliation: Seoul National University Biomedical Informatics, Seoul, Korea.
authors.forename: Sang Jay
authors.initials: SJ
authors.lastname: Bien
authors.forename: Chan Hee
authors.initials: CH
authors.lastname: Park
authors.forename: Hae Jin
authors.initials: HJ
authors.lastname: Shim
authors.forename: Woongcheol
authors.initials: W
authors.lastname: Yang
authors.forename: Jihun
authors.initials: J
authors.lastname: Kim
authors.forename: Ju Han
authors.initials: JH
authors.lastname: Kim
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D020869:Gene Expression Profiling; D006801:Humans; D009323:Natural Language Processing; D011506:Proteins / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results; D012660:Semantics; D018875:Vocabulary, Controlled
chemical_list: D011506:Proteins