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Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm
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 effec...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5309434/ https://www.ncbi.nlm.nih.gov/pubmed/28255556 http://dx.doi.org/10.1155/2017/6132436 |
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author | Guo, Wei Shang, Dong-Mei Cao, Jing-Hui Feng, Kaiyan He, Yi-Chun Jiang, Yang Wang, ShaoPeng Gao, Yu-Fei |
author_facet | Guo, Wei Shang, Dong-Mei Cao, Jing-Hui Feng, Kaiyan He, Yi-Chun Jiang, Yang Wang, ShaoPeng Gao, Yu-Fei |
author_sort | Guo, Wei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5309434 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-53094342017-03-02 Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm Guo, Wei Shang, Dong-Mei Cao, Jing-Hui Feng, Kaiyan He, Yi-Chun Jiang, Yang Wang, ShaoPeng Gao, Yu-Fei Biomed Res Int Research Article 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. Hindawi Publishing Corporation 2017 2017-02-01 /pmc/articles/PMC5309434/ /pubmed/28255556 http://dx.doi.org/10.1155/2017/6132436 Text en Copyright © 2017 Wei Guo et al. https://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Research Article Guo, Wei Shang, Dong-Mei Cao, Jing-Hui Feng, Kaiyan He, Yi-Chun Jiang, Yang Wang, ShaoPeng Gao, Yu-Fei Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm |
title | Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm |
title_full | Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm |
title_fullStr | Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm |
title_full_unstemmed | Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm |
title_short | Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm |
title_sort | identifying and analyzing novel epilepsy-related genes using random walk with restart algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5309434/ https://www.ncbi.nlm.nih.gov/pubmed/28255556 http://dx.doi.org/10.1155/2017/6132436 |
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