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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.

Supplementary Material

The Supplementary Material consists of three files. In detail, Supplementary Material S1 lists 470 genes related to epilepsy with their Ensembl IDs and gene symbols; Supplementary Material S2 lists 6,886 RWR genes derived from RWR algorithm with probability larger than 1E-05; Supplementary Material S3 lists 980 candidate genes with permutation FDRs less than 0.05.

  1. Supplementary Material
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