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BioMed Research International
Volume 2017, Article ID 6132436, 13 pages
https://doi.org/10.1155/2017/6132436
Research Article

Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm

1Department of Outpatient, China-Japan Union Hospital of Jilin University, Changchun 130033, China
2Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
3Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou 510507, China
4Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
5School of Life Sciences, Shanghai University, Shanghai 200444, China

Correspondence should be addressed to Yu-Fei Gao; nc.anis@5791iefuyoag

Received 23 October 2016; Accepted 15 January 2017; Published 1 February 2017

Academic Editor: Ansgar Poetsch

Copyright © 2017 Wei Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients’ personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.