title: Characterisation of semantic similarity on gene ontology based on a shortest path approach.
abstract: Semantic similarity defined on Gene Ontology (GO) aims to provide the functional relationship between different GO terms. In this paper, a novel method, namely the Shortest Path (SP) algorithm, for measuring the semantic similarity on GO terms is proposed based on both GO structure information and the term's property. The proposed algorithm searches for the shortest path that connects two terms and uses the sum of weights on the path to estimate the semantic similarity between GO terms. A method for evaluating the nonlinear correlation between two variables is also introduced for validation. Extensive experiments conducted on the PPI dataset and two public gene expression datasets demonstrate the overall superiority of SP method over the other state-of-the-art methods evaluated.
authors.forename: Ying
authors.initials: Y
authors.lastname: Shen
authors.forename: Shaohong
authors.initials: S
authors.lastname: Zhang
authors.forename: Hau-San
authors.initials: HS
authors.lastname: Wong
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D005656:Fungal Proteins / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D005800:Genes, Fungal; D008957:Models, Genetic; D025941:Protein Interaction Mapping; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D012660:Semantics; D018875:Vocabulary, Controlled
chemical_list: D005656:Fungal Proteins