Deep learning for cancer type classification and driver gene identification.

Journal: BMC bioinformatics
Published Date: Oct 25, 2021

Abstract

BACKGROUND: Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction.

Authors

  • Zexian Zeng
    Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Chengsheng Mao
    Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
  • Andy Vo
    Committee on Developmental Biology and Regenerative Medicine, The University of Chicago, Chicago, IL, USA.
  • Xiaoyu Li
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Janna Ore Nugent
    Research Computing Services, Northwestern University, Chicago, IL, USA.
  • Seema A Khan
    Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Susan E Clare
    Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Yuan Luo
    Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.

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