title: Network propagation with dual flow for gene prioritization.
abstract: Based on the hypothesis that the neighbors of disease genes trend to cause similar diseases, network-based methods for disease prediction have received increasing attention. Taking full advantage of network structure, the performance of global distance measurements is generally superior to local distance measurements. However, some problems exist in the global distance measurements. For example, global distance measurements may mistake non-disease hub proteins that have dense interactions with known disease proteins for potential disease proteins. To find a new method to avoid the aforementioned problem, we analyzed the differences between disease proteins and other proteins by using essential proteins (proteins encoded by essential genes) as references. We find that disease proteins are not well connected with essential proteins in the protein interaction networks. Based on this new finding, we proposed a novel strategy for gene prioritization based on protein interaction networks. We allocated positive flow to disease genes and negative flow to essential genes, and adopted network propagation for gene prioritization. Experimental results on 110 diseases verified the effectiveness and potential of the proposed method.
authors.affiliation: College of Automation Engineering, Qingdao University, Qingdao, China; College of Information Engineering, Qingdao University, Qingdao, China.
authors.forename: Shunyao
authors.initials: S
authors.lastname: Wu
authors.affiliation: College of Automation Engineering, Qingdao University, Qingdao, China; College of Information Engineering, Qingdao University, Qingdao, China.
authors.forename: Fengjing
authors.initials: F
authors.lastname: Shao
authors.affiliation: College of Information Engineering, Qingdao University, Qingdao, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Ji
authors.affiliation: College of Information Engineering, Qingdao University, Qingdao, China.
authors.forename: Rencheng
authors.initials: R
authors.lastname: Sun
authors.affiliation: School of Computer Engineering, Qingdao Technological University, Qingdao, China.
authors.forename: Rizhuang
authors.initials: R
authors.lastname: Dong
authors.affiliation: College of Information Engineering, Qingdao University, Qingdao, China.
authors.forename: Yuanke
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: College of Information Engineering, Qingdao University, Qingdao, China.
authors.forename: Shaojie
authors.initials: S
authors.lastname: Xu
authors.affiliation: College of Information Engineering, Qingdao University, Qingdao, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Sui
authors.affiliation: College of Information Engineering, Qingdao University, Qingdao, China.
authors.forename: Jianlong
authors.initials: J
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies; D030342:Genetic Diseases, Inborn / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D056784:Leukoencephalopathies / Q000235:genetics / Q000378:metabolism; D015233:Models, Statistical; D060066:Protein Interaction Maps; D012372:ROC Curve; D015203:Reproducibility of Results