Title: Characterisation of semantic similarity on gene ontology based on a shortest path approach

Authors: Ying Shen; Shaohong Zhang; Hau-San Wong; Lin Zhang

Addresses: School of Software Engineering, Tongji University, Shanghai, P.R. China ' Department of Computer Science, Guangzhou University, Guangzhou, P.R. China ' Department of Computer Science, City University of Hong Kong, Hong Kong ' School of Software Engineering, Tongji University, Shanghai, P.R. China

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.

Keywords: gene ontology; shortest path; semantic similarity; protein-protein interaction; PPI; gene expression.

DOI: 10.1504/IJDMB.2014.062887

International Journal of Data Mining and Bioinformatics, 2014 Vol.10 No.1, pp.33 - 48

Received: 21 Apr 2011
Accepted: 29 Apr 2011

Published online: 21 Oct 2014 *

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