SWE: a novel method with semantic-weighted edge for measuring gene functional similarity

Zhen Tian, Haichuan Fang, Yangdong Ye, Zhenfeng Zhu

Abstract: In recent years, functional similarity has played an independent role in some biological fields such as gene clustering, gene functional prediction, and evaluation for proteinprotein interaction. In this premise, some effective methods have already been proposed based on Gene Ontology (GO). Although these mainstream methods achieve the purpose for measuring gene functional similarity, they may have some deficiency when calculating the Information Content (IC) of GO terms. Consequently, measuring the functional similarity accurately is still a meaningful objective of research. In this paper, a novel method called SWE, is proposed for measuring gene functional similarity based on the GO graph. Firstly, an algorithm to measure terms’ semantics based on their information in the GO graph is put forward. The information of GO terms mainly contains their depth, ancestors and descendants. Secondly, we calculate the IC of a term set by means of retrieving the inherited relationship between terms in a term set. Finally, the functional similarity between two genes is computed based on the IC overlap ratio of term sets annotating two genes respectively. Results demonstrate that SWE is superior to existing methods in some experiments such as functional classification of genes in a biological pathway, protein-protein interaction and gene expression experiment. Further analysis demonstrates that SWE takes not only the specificity of terms into account, but their information in the GO graph, both of which are shown to be consistent with human perspectives.