Prioritizing Cancer Genes Based on an Improved Random Walk Method - Wei, Pi-Jing; Wu, Fang-Xiang; Xia, Junfeng; Su, Yansen; Wang, Jing; Zheng, Chun-Hou (Frontiers in Genetics, 2020)
It is a central goal but a major challenging task to identify driver genes that contribute to cancer progression from numerous passenger genes. Protein-protein interaction network provides a convenient and reasonable assistance for driver genes discovery. In random walk -based methods have been widely used to prioritize nodes in social networks or biological networks. However, most of them select the next arriving node uniformly from the random walker’s neighbors. Few of them consider transiting preference according to the degree of random walker’s neighbors. In this study, based on random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes’ neighbors. Furthermore, the global centrality betweenness centrality and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs efficiently compared with some published methods in uncovering known cancer related genes. In conclusion, our method can aid in prioritizing cancer related genes and complement to traditional frequency and network -based methods.