A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data.

Journal: BMC bioinformatics
Published Date: Jul 26, 2022

Abstract

BACKGROUND: The discovery of critical biomarkers is significant for clinical diagnosis, drug research and development. Researchers usually obtain biomarkers from microarray data, which comes from the dimensional curse. Feature selection in machine learning is usually used to solve this problem. However, most methods do not fully consider feature dependence, especially the real pathway relationship of genes.

Authors

  • Weidong Xie
    School of Computer Science and Engineering, Northeastern University, Shenyang, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Shoujia Zhang
    School of Computer Science and Engineering, Northeastern University, Shenyang, China.
  • Linjie Wang
    School of Computer Science and Engineering, Northeastern University, Shenyang, China.
  • Jinzhu Yang
    College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
  • Dazhe Zhao
    Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.

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