Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution.

Journal: BMC bioinformatics
Published Date: Jul 16, 2025

Abstract

BACKGROUND: Inferring Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology. Most existing methods fail to consider the skewed degree distribution of genes, complicating the application of directed graph embedding methods.

Authors

  • Jiaqi Xiong
    Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Guangzhou, 528225, China.
  • Nan Yin
    Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China. yinnan8911@gmail.com.
  • Shiyang Liang
    Department of Internal Medicine, The No. 944 Hospital of Joint Logistic Support Force of PLA, Xiongguan Road, Jiu Quan, 735000, China.
  • Haoyang Li
  • Yingxu Wang
    Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an 710049, China.
  • Duo Ai
    Department of Dermatology, Xijing Hospital, Fourth Military Medical University, No 127 of West Changle Road, Xi'an, 710032, Shaanxi, China.
  • Jingjie Wang
    Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China.

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