A New Semantic Functional Similarity over Gene Ontology

Abstract

Identifying functionally similar or closely related genes and gene products has significant impacts on biological and clinical studies as well as drug discovery. In this paper, we propose an effective and practically useful method measuring both gene and gene product similarity by integrating the topology of gene ontology, known functional domains and their functional annotations. The proposed method is comprehensively evaluated through statistical analysis of the similarities derived from sequence, structure and phylogenetic profiles, and clustering analysis of disease genes clusters. Our results show that the proposed method clearly outperforms other conventional methods. Furthermore, literature analysis also reveals that the proposed method is both statistically and biologically promising for identifying functionally similar genes or gene products. In particular, we demonstrate that the proposed functional similarity metric is capable of discoverying new disease related genes or gene products.

An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations

“…DO is usually selected as the source of disease terms for the disease similarity calculation. Several previous approaches, including those based on information content (IC) (Resnik, 1995; Lin, 1998; Schlicker et al, 2006; Wang et al, 2007; Bandyopadhyay and Mallick, 2014), ontology Directed Acyclic Graph (DAG) structure (Kim et al, 1993; Zhang et al, 2010; Santos et al, 2012) and biological function process (Mathur and Dinakarpandian, 2012; Cheng et al, 2014; Jeong and Chen, 2015; Zou et al, 2016; Yang et al, 2017; Ni et al, 2018), have been proposed with the aim to measure the disease similarity by using DO. For the IC-based approaches, Resnik (1995) use IC of the most informative common ancestor (MICA) to measure the similarity of two diseases.…”