An Improved Method for Gene Function Network Term Similarity Calculation

Background Gene Ontology (GO) is an ontology based on bioinformatics resources that utilizes its structure to represent biological knowledge and describe the functions of genes and gene products. The computation of term similarity within Gene Ontology plays a critical role in various biological research areas, such as gene function analysis, comparison, and prediction. However, existing algorithms for term similarity calculation have several limitations and fail to fully exploit available information. In recent years, some studies have incorporated gene functional networks into term similarity calculations; however, these approaches typically focus only on directly connected genes, overlooking indirect relationships within the gene network and failing to make optimal use of all available data. Results In this study, we propose a novel Gene Ontology term similarity algorithm based on a Random Walk with Restart (RWR) framework, enhanced by a Gaussian kernel function (RWRSM). This algorithm not only incorporates structural and annotation information from Gene Ontology but also captures global structural information from gene functional networks. We performed multiple experiments on yeast and Arabidopsis datasets using Enzyme Commission (EC) classification numbers. Conclusion The experimental results demonstrate that our proposed algorithm outperforms existing methods across all measures for both yeast and Arabidopsis datasets. Specifically, the Local Function Consistency (LFC) results are more stable, and our method uncovers a greater number of meaningful gene associations.