Comparison of four similarity measures based on GO annotations for Gene Clustering

Abstract

Gene ontology (GO) has fast become a dependable source for determining gene functions, gene similarity, and gene clustering. Furthermore, using GO and gene annotation databases, with semantic similarity measures, is now more acceptable in bioinformatics as means for gene functional analysis. In this paper, we compare four semantic similarity measures to compute the similarity between genes using GO annotations within a gene clustering application. The similarity measures we examined in this study rely on different information sources and techniques in computing the similarity between genes. For example, we selected two measures that are based on information-content, one measure is based on ontology-structure, and the fourth measure is based on annotation terms within the GO hierarchy. In the evaluation, we used five gene datasets from yeast genome, and we analyzed the results based on various clustering metrics and criteria.