MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.

Journal: Bioinformatics (Oxford, England)
Published Date: Aug 1, 2023

Abstract

MOTIVATION: CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area of research. Due to the high cost of validating circRNA-disease associations using traditional wet-lab experiments, novel computational methods based on machine learning are gaining more and more attention in this field. However, current computational methods suffer to insufficient consideration of latent features in circRNA-disease interactions.

Authors

  • Qunzhuo Wu
    School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
  • Zhaohong Deng
    School of Digital Media, Jiangnan University, Wuxi, Jiangsu, China.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Xiaoyong Pan
    Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Copenhagen, Denmark. xypan172436@gmail.com.
  • Kup-Sze Choi
    Centre for Smart Heath, School of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
  • Yun Zuo
    Department of Mathematics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China.
  • Hong-Bin Shen
    Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China. hbshen@sjtu.edu.cn.
  • Dong-Jun Yu

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