Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.

Journal: BMC genomics
Published Date: Aug 13, 2018

Abstract

BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance.

Authors

  • Yuchen Yuan
    School of Information Technologies, The University of Sydney, Darlington, NSW, 2008, Australia.
  • Yi Shi
    College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, People's Republic of China.
  • Xianbin Su
    Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.
  • Xin Zou
    Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.
  • Qing Luo
    Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.
  • David Dagan Feng
  • Weidong Cai
    School of Computer Science, The University of Sydney, Darlington, WA, Australia.
  • Ze-Guang Han
    Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China. hanzg@sjtu.edu.cn.

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