SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction.

Journal: BMC bioinformatics
PMID: 39930351

Abstract

BACKGROUND: A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncovering potential function relationships between distant proteins within PPI networks is essential for improving the accuracy of protein function prediction. Most current studies attempt to capture these distant relationships by stacking graph network layers, but performance gains diminish as the number of layers increases.

Authors

  • Yansong Wang
    School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai, 264209, China.
  • Yundong Sun
    Department of Electronic Science and Technology, Harbin Institute of Technology, Harbin, 150001, China; School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai, 264209, China.
  • Baohui Lin
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
  • Haotian Zhang
  • Xiaoling Luo
    Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
  • Yumeng Liu
    School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
  • Xiaopeng Jin
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518055, China.
  • Dongjie Zhu
    School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai, 264209, China. Electronic address: zhudongjie@hit.edu.cn.

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