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Comprehensive evaluation of computational methods for predicting cancer driver genes

Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Various computational methods based on mutational frequency, network and function approaches have been developed to identify mutation driver genes in cancer genomes. However, a comprehensive...

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Detalles Bibliográficos
Autores principales: Shi, Xiaohui, Teng, Huajing, Shi, Leisheng, Bi, Wenjian, Wei, Wenqing, Mao, Fengbiao, Sun, Zhongsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921613/
https://www.ncbi.nlm.nih.gov/pubmed/35037014
http://dx.doi.org/10.1093/bib/bbab548
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author Shi, Xiaohui
Teng, Huajing
Shi, Leisheng
Bi, Wenjian
Wei, Wenqing
Mao, Fengbiao
Sun, Zhongsheng
author_facet Shi, Xiaohui
Teng, Huajing
Shi, Leisheng
Bi, Wenjian
Wei, Wenqing
Mao, Fengbiao
Sun, Zhongsheng
author_sort Shi, Xiaohui
collection PubMed
description Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Various computational methods based on mutational frequency, network and function approaches have been developed to identify mutation driver genes in cancer genomes. However, a comprehensive evaluation of the performance levels of network-, function- and frequency-based methods is lacking. In the present study, we assessed and compared eight performance criteria for eight network-based, one function-based and three frequency-based algorithms using eight benchmark datasets. Under different conditions, the performance of approaches varied in terms of network, measurement and sample size. The frequency-based driverMAPS and network-based HotNet2 methods showed the best overall performance. Network-based algorithms using protein–protein interaction networks outperformed the function- and the frequency-based approaches. Precision, F1 score and Matthews correlation coefficient were low for most approaches. Thus, most of these algorithms require stringent cutoffs to correctly distinguish driver and non-driver genes. We constructed a website named Cancer Driver Catalog (http://159.226.67.237/sun/cancer_driver/), wherein we integrated the gene scores predicted by the foregoing software programs. This resource provides valuable guidance for cancer researchers and clinical oncologists prioritizing cancer driver gene candidates by using an optimal tool.
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spelling pubmed-89216132022-03-15 Comprehensive evaluation of computational methods for predicting cancer driver genes Shi, Xiaohui Teng, Huajing Shi, Leisheng Bi, Wenjian Wei, Wenqing Mao, Fengbiao Sun, Zhongsheng Brief Bioinform Problem Solving Protocol Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Various computational methods based on mutational frequency, network and function approaches have been developed to identify mutation driver genes in cancer genomes. However, a comprehensive evaluation of the performance levels of network-, function- and frequency-based methods is lacking. In the present study, we assessed and compared eight performance criteria for eight network-based, one function-based and three frequency-based algorithms using eight benchmark datasets. Under different conditions, the performance of approaches varied in terms of network, measurement and sample size. The frequency-based driverMAPS and network-based HotNet2 methods showed the best overall performance. Network-based algorithms using protein–protein interaction networks outperformed the function- and the frequency-based approaches. Precision, F1 score and Matthews correlation coefficient were low for most approaches. Thus, most of these algorithms require stringent cutoffs to correctly distinguish driver and non-driver genes. We constructed a website named Cancer Driver Catalog (http://159.226.67.237/sun/cancer_driver/), wherein we integrated the gene scores predicted by the foregoing software programs. This resource provides valuable guidance for cancer researchers and clinical oncologists prioritizing cancer driver gene candidates by using an optimal tool. Oxford University Press 2022-01-17 /pmc/articles/PMC8921613/ /pubmed/35037014 http://dx.doi.org/10.1093/bib/bbab548 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Problem Solving Protocol
Shi, Xiaohui
Teng, Huajing
Shi, Leisheng
Bi, Wenjian
Wei, Wenqing
Mao, Fengbiao
Sun, Zhongsheng
Comprehensive evaluation of computational methods for predicting cancer driver genes
title Comprehensive evaluation of computational methods for predicting cancer driver genes
title_full Comprehensive evaluation of computational methods for predicting cancer driver genes
title_fullStr Comprehensive evaluation of computational methods for predicting cancer driver genes
title_full_unstemmed Comprehensive evaluation of computational methods for predicting cancer driver genes
title_short Comprehensive evaluation of computational methods for predicting cancer driver genes
title_sort comprehensive evaluation of computational methods for predicting cancer driver genes
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921613/
https://www.ncbi.nlm.nih.gov/pubmed/35037014
http://dx.doi.org/10.1093/bib/bbab548
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