Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

Journal: Bioinformatics (Oxford, England)
PMID: 38940121

Abstract

MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.

Authors

  • Chao Deng
    School of Mechanical Science & Engineering, Huazhong University Of Science & Technology, 1037 Luoyu Road, Wuhan, China. Electronic address: dengchao@hust.edu.cn.
  • Hong-Dong Li
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Li-Shen Zhang
    School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
  • Yiwei Liu
    School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.
  • Yaohang Li
  • Jianxin Wang

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