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Prioritizing Cancer Genes Based on an Improved Random Walk Method
Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein–protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk–based methods have been widely use...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198854/ https://www.ncbi.nlm.nih.gov/pubmed/32411180 http://dx.doi.org/10.3389/fgene.2020.00377 |
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author | Wei, Pi-Jing Wu, Fang-Xiang Xia, Junfeng Su, Yansen Wang, Jing Zheng, Chun-Hou |
author_facet | Wei, Pi-Jing Wu, Fang-Xiang Xia, Junfeng Su, Yansen Wang, Jing Zheng, Chun-Hou |
author_sort | Wei, Pi-Jing |
collection | PubMed |
description | Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein–protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk–based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods. |
format | Online Article Text |
id | pubmed-7198854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71988542020-05-14 Prioritizing Cancer Genes Based on an Improved Random Walk Method Wei, Pi-Jing Wu, Fang-Xiang Xia, Junfeng Su, Yansen Wang, Jing Zheng, Chun-Hou Front Genet Genetics Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein–protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk–based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods. Frontiers Media S.A. 2020-04-28 /pmc/articles/PMC7198854/ /pubmed/32411180 http://dx.doi.org/10.3389/fgene.2020.00377 Text en Copyright © 2020 Wei, Wu, Xia, Su, Wang and Zheng. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wei, Pi-Jing Wu, Fang-Xiang Xia, Junfeng Su, Yansen Wang, Jing Zheng, Chun-Hou Prioritizing Cancer Genes Based on an Improved Random Walk Method |
title | Prioritizing Cancer Genes Based on an Improved Random Walk Method |
title_full | Prioritizing Cancer Genes Based on an Improved Random Walk Method |
title_fullStr | Prioritizing Cancer Genes Based on an Improved Random Walk Method |
title_full_unstemmed | Prioritizing Cancer Genes Based on an Improved Random Walk Method |
title_short | Prioritizing Cancer Genes Based on an Improved Random Walk Method |
title_sort | prioritizing cancer genes based on an improved random walk method |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198854/ https://www.ncbi.nlm.nih.gov/pubmed/32411180 http://dx.doi.org/10.3389/fgene.2020.00377 |
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