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AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutat...

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Autores principales: Wang, Haoxuan, Wang, Tao, Zhao, Xiaolu, Wu, Honghu, You, Mingcong, Sun, Zhongsheng, Mao, Fengbiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671397/
https://www.ncbi.nlm.nih.gov/pubmed/33575629
http://dx.doi.org/10.1093/nargab/lqaa084
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author Wang, Haoxuan
Wang, Tao
Zhao, Xiaolu
Wu, Honghu
You, Mingcong
Sun, Zhongsheng
Mao, Fengbiao
author_facet Wang, Haoxuan
Wang, Tao
Zhao, Xiaolu
Wu, Honghu
You, Mingcong
Sun, Zhongsheng
Mao, Fengbiao
author_sort Wang, Haoxuan
collection PubMed
description The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.
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spelling pubmed-76713972021-02-10 AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes Wang, Haoxuan Wang, Tao Zhao, Xiaolu Wu, Honghu You, Mingcong Sun, Zhongsheng Mao, Fengbiao NAR Genom Bioinform Standard Article The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy. Oxford University Press 2020-10-13 /pmc/articles/PMC7671397/ /pubmed/33575629 http://dx.doi.org/10.1093/nargab/lqaa084 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. http://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 (http://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 Standard Article
Wang, Haoxuan
Wang, Tao
Zhao, Xiaolu
Wu, Honghu
You, Mingcong
Sun, Zhongsheng
Mao, Fengbiao
AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
title AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
title_full AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
title_fullStr AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
title_full_unstemmed AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
title_short AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
title_sort ai-driver: an ensemble method for identifying driver mutations in personal cancer genomes
topic Standard Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671397/
https://www.ncbi.nlm.nih.gov/pubmed/33575629
http://dx.doi.org/10.1093/nargab/lqaa084
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