NRSSPrioritize: Associating Protein Complex and Disease Similarity Information to Prioritize Disease Candidate Genes

Abstract

The identification of disease-associated genes has recently gathered much attention for uncovering disease complex mechanisms that could lead to new insights into the treatment of diseases. For exploring disease-susceptible genes, not only experimental approaches such as genome-wide association studies (GWAS) have been used, but also computational methods. Since experimental approaches are both time-consuming and expensive, numerous studies have utilized computational techniques to explore disease genes. These methods use various biological data sources and known disease genes to prioritize disease candidate genes. In this paper, we propose a gene prioritization method (NRSSPrioritize), which benefits from both local and global measures of a protein-protein interaction (PPI) network and also from disease similarity knowledge to suggest candidate genes for colorectal cancer (CRC) susceptibility. Network Propagation, Random Walk with Restart, and Shortest Paths are three network analysis tools that are applied to a PPI network for the purpose of scoring candidate genes. Also, by looking through diseases with similar symptoms to CRC and obtaining their causing genes, candidate genes are scored in a different way. Finally, to integrate these four different scoring schemes, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Network Process (ANP) methods are applied to obtain appropriate weights for the above four quantified measures and the weighted summation of these measures are used to calculate the final score of each candidate gene. NRSSPrioritize was validated by cross-validation analysis and its results were compared with other prioritization tools, which gave the best performance when using our proposed method.