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

Authors:
Haiying Wang;Francisco Azuaje;Huiru Zheng
Affiliations:
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
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2014

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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.