An integrative approach for measuring semantic similarities using gene ontology

Background: Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding. Results: We propose a novel integrative measure called InteGO 2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO 2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories. Conclusions: InteGO 2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.