title: Prioritization of candidate disease genes by combining topological similarity and semantic similarity.
abstract: The identification of gene-phenotype relationships is very important for the treatment of human diseases. Studies have shown that genes causing the same or similar phenotypes tend to interact with each other in a protein-protein interaction (PPI) network. Thus, many identification methods based on the PPI network model have achieved good results. However, in the PPI network, some interactions between the proteins encoded by candidate gene and the proteins encoded by known disease genes are very weak. Therefore, some studies have combined the PPI network with other genomic information and reported good predictive performances. However, we believe that the results could be further improved. In this paper, we propose a new method that uses the semantic similarity between the candidate gene and known disease genes to set the initial probability vector of a random walk with a restart algorithm in a human PPI network. The effectiveness of our method was demonstrated by leave-one-out cross-validation, and the experimental results indicated that our method outperformed other methods. Additionally, our method can predict new causative genes of multifactor diseases, including Parkinson's disease, breast cancer and obesity. The top predictions were good and consistent with the findings in the literature, which further illustrates the effectiveness of our method.
authors.affiliation: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.affiliation: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. Electronic address: jinmin@hnu.edu.cn.
authors.forename: Min
authors.initials: M
authors.lastname: Jin
authors.affiliation: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
authors.forename: Pan
authors.initials: P
authors.lastname: Zeng
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D004194:Disease / Q000235:genetics*; D005796:Genes; D056726:Genetic Association Studies; D006801:Humans; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D012660:Semantics
keywords: Disease genes; Random walk; Semantic similarity; Topological similarity