Deep convolutional neural networks for accurate somatic mutation detection

Somatic mutations are crucial to the understanding of cancer genesis, progression, and treatment, but are still challenging to detect. Here the authors present NeuSomatic, a convolutional neural network approach for accurate somatic mutation detection across various sequencing scenarios.

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Detalles Bibliográficos
Autores principales: Sayed Mohammad Ebrahim Sahraeian, Ruolin Liu, Bayo Lau, Karl Podesta, Marghoob Mohiyuddin, Hugo Y. K. Lam
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/97e2f4d53235488391827e07f5a633f0
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Sumario:Somatic mutations are crucial to the understanding of cancer genesis, progression, and treatment, but are still challenging to detect. Here the authors present NeuSomatic, a convolutional neural network approach for accurate somatic mutation detection across various sequencing scenarios.