DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.

Journal: BMC biology
PMID: 40264118

Abstract

BACKGROUND: Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due to the high cost and time-consuming nature of traditional wet lab experiments, analyzing circRNA-miRNA associations is often inefficient and labor-intensive. Although some computational models have been developed to identify these associations, they fail to capture the deep collaborative features between circRNA and miRNA interactions and do not guide the training of feature extraction networks based on these high-order relationships, leading to poor prediction performance.

Authors

  • Chao Cao
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Mengli Li
    School of Technology, Guilin University, Guilin, China.
  • Chunyu Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
  • Quan Zou
  • Yansu Wang
    Postdoctoral Innovation Practice Base, Shenzhen Polytechnic, China.
  • Wu Han
    Department of Statistics, Stanford University, Stanford, CA, 94043, USA. kevinwh@stanford.edu.

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