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Deep convolutional neural networks for accurate somatic mutation detection

Author

Listed:
  • Sayed Mohammad Ebrahim Sahraeian

    (Roche Sequencing Solutions)

  • Ruolin Liu

    (Roche Sequencing Solutions)

  • Bayo Lau

    (Roche Sequencing Solutions)

  • Karl Podesta

    (Microsoft Azure)

  • Marghoob Mohiyuddin

    (Roche Sequencing Solutions)

  • Hugo Y. K. Lam

    (Roche Sequencing Solutions)

Abstract

Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.

Suggested Citation

  • Sayed Mohammad Ebrahim Sahraeian & Ruolin Liu & Bayo Lau & Karl Podesta & Marghoob Mohiyuddin & Hugo Y. K. Lam, 2019. "Deep convolutional neural networks for accurate somatic mutation detection," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09027-x
    DOI: 10.1038/s41467-019-09027-x
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    Cited by:

    1. Kiran Krishnamachari & Dylan Lu & Alexander Swift-Scott & Anuar Yeraliyev & Kayla Lee & Weitai Huang & Sim Ngak Leng & Anders Jacobsen Skanderup, 2022. "Accurate somatic variant detection using weakly supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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