Deep learning approach for cancer subtype classification using high-dimensional gene expression data.

Journal: BMC bioinformatics
Published Date: Oct 17, 2022

Abstract

MOTIVATION: Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results.

Authors

  • Jiquan Shen
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China. sjq@hpu.edu.cn.
  • Jiawei Shi
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Junwei Luo
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Haixia Zhai
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Xiaoyan Liu
    College of Information Technology, Jilin Agricultural University, Changchun, China.
  • Zhengjiang Wu
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Chaokun Yan
    School of Computer Science and Information Engineering, Henan University, Kaifeng, 475001, China.
  • Huimin Luo

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