Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.

Journal: BMC biology
PMID: 40369616

Abstract

BACKGROUND: Different expression levels of circular RNAs (circRNAs) affect the sensitivity of human cells to drugs, thus producing different responses to the therapeutic effects of drugs. Using traditional biomedical experiments to discover and confirm sensitivity relationships is not only time-consuming but also costly. Therefore, developing an effective method to accurately predict new associations between circRNAs and drug sensitivity is crucial and urgent. Therefore, we constructed a heterogeneous graph network MiGNN2CDS on the basis of multi-instance learning (MIL).

Authors

  • Mengting Niu
    School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China. yunzeer@gmail.com.
  • Chunyu Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Yaojia Chen
    College of Electronics and Information Engineering Guangdong Ocean University, Zhanjiang, China and the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
  • Quan Zou
  • Ximei Luo
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.

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