Title: An information theoretic approach to assessing gene-ontology-driven similarity and its application

Authors: Haiying Wang; Francisco Azuaje; Huiru Zheng

Addresses: Computer Science Research Institute, School of Computing and Mathematics, University of Ulster at Jordanstown, Northern Ireland, UK ' Laboratory of Cardiovascular Research, Public Research Centre for Health (CRP-Santé), L-1150, Luxembourg ' Computer Science Research Institute, School of Computing and Mathematics, University of Ulster at Jordanstown, Northern Ireland, UK

Abstract: Using information-theoretic approaches, this paper presents a cross-platform system to support the integration of Gene Ontology (GO)-driven similarity knowledge into functional genomics. Three GO-driven similarity measures (Resnik's, Lin's and Jiang's metrics) have been implemented to measure between-term similarity within each of the GO hierarchies. Two approaches (simple and highest average similarity) which are based on the aggregation of between-term similarities, are used to estimate the similarity between gene products. The system has been successfully applied to a number of applications including assessing gene expression correlation patterns and the relationships between GO-driven similarity and other functional properties.

Keywords: semantic similarity; gene ontology; information content; information theory; functional genomics; gene expression correlation patterns; bioinformatics.

DOI: 10.1504/IJDMB.2014.059061

International Journal of Data Mining and Bioinformatics, 2014 Vol.9 No.2, pp.121 - 134

Received: 29 Apr 2011
Accepted: 29 Apr 2011
Published online: 21 Oct 2014 *

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