title: Identifying and prioritizing disease-related genes based on the network topological features.
abstract: Identifying and prioritizing disease-related genes are the most important steps for understanding the pathogenesis and discovering the therapeutic targets. The experimental examination of these genes is very expensive and laborious, and usually has a higher false positive rate. Therefore, it is highly desirable to develop computational methods for the identification and prioritization of disease-related genes. In this study, we develop a powerful method to identify and prioritize candidate disease genes. The novel network topological features with local and global information are proposed and adopted to characterize genes. The performance of these novel features is verified based on the 10-fold cross-validation test and leave-one-out cross-validation test. The proposed features are compared with the published features, and fused strategy is investigated by combining the current features with the published features. And, these combination features are also utilized to identify and prioritize Parkinson's disease-related genes. The results indicate that identified genes are highly related to some molecular process and biological function, which provides new clues for researching pathogenesis of Parkinson's disease. The source code of Matlab is freely available on request from the authors.
authors.affiliation: School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China. Electronic address: zhanchao8052@gmail.com.
authors.forename: Zhan-Chao
authors.initials: ZC
authors.lastname: Li
authors.affiliation: School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Yan-Hua
authors.initials: YH
authors.lastname: Lai
authors.affiliation: School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Li-Li
authors.initials: LL
authors.lastname: Chen
authors.affiliation: School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Xie
authors.affiliation: School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Zong
authors.initials: Z
authors.lastname: Dai
authors.affiliation: School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China. Electronic address: ceszxy@mail.sysu.edu.cn.
authors.forename: Xiao-Yong
authors.initials: XY
authors.lastname: Zou
keywords: Gene ontology; Graph theory; Parkinson's disease; Support vector machine; Topology