BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism.

Journal: Computational biology and chemistry
Published Date: Sep 1, 2022

Abstract

BACKGROUND: RNA Secondary Structure (RSS) has drawn growing concern, both for their pivotal roles in RNA tertiary structures prediction and critical effect in penetrating the mechanism of functional non-coding RNA. Computational techniques that can reduce the in vitro and in vivo experimental costs have become popular in RSS prediction. However, as an NP-hard problem, there is room for improvement that the validity of the prediction RSS with pseudoknots in traditional machine learning predictors.

Authors

  • Cong Shen
    Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA;
  • Yu Chen
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • Feng Xiao
    Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
  • Tian Yang
    Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China.
  • Xinyue Wang
    Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China.
  • Shengyong Chen
  • Jijun Tang
    School of Computer Science and Engineering, Tianjin University, Tianjin, 300072, China. jtang@cse.sc.edu.
  • Zhijun Liao
    Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian 350122, China.

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