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MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and mole...

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Detalles Bibliográficos
Autores principales: Hou, Yingnan, Gao, Bo, Li, Guojun, Su, Zhengchang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6145398/
https://www.ncbi.nlm.nih.gov/pubmed/30250803
http://dx.doi.org/10.1002/advs.201800640
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author Hou, Yingnan
Gao, Bo
Li, Guojun
Su, Zhengchang
author_facet Hou, Yingnan
Gao, Bo
Li, Guojun
Su, Zhengchang
author_sort Hou, Yingnan
collection PubMed
description Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan‐Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state‐of‐the‐art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top‐ranked candidate genes than the best among the other tools evaluated. MaxMIF is also highly robust to data perturbation. Intriguingly, MaxMIF is able to identify potential cancer driver genes, with strong experimental data support. Therefore, MaxMIF can be very useful for identifying or prioritizing cancer driver genes in the increasing number of available cancer genomic data.
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spelling pubmed-61453982018-09-24 MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration Hou, Yingnan Gao, Bo Li, Guojun Su, Zhengchang Adv Sci (Weinh) Full Papers Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan‐Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state‐of‐the‐art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top‐ranked candidate genes than the best among the other tools evaluated. MaxMIF is also highly robust to data perturbation. Intriguingly, MaxMIF is able to identify potential cancer driver genes, with strong experimental data support. Therefore, MaxMIF can be very useful for identifying or prioritizing cancer driver genes in the increasing number of available cancer genomic data. John Wiley and Sons Inc. 2018-07-23 /pmc/articles/PMC6145398/ /pubmed/30250803 http://dx.doi.org/10.1002/advs.201800640 Text en © 2018 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Full Papers
Hou, Yingnan
Gao, Bo
Li, Guojun
Su, Zhengchang
MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration
title MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration
title_full MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration
title_fullStr MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration
title_full_unstemmed MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration
title_short MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration
title_sort maxmif: a new method for identifying cancer driver genes through effective data integration
topic Full Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6145398/
https://www.ncbi.nlm.nih.gov/pubmed/30250803
http://dx.doi.org/10.1002/advs.201800640
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