Deep learning based method for predicting DNA N6-methyladenosine sites.

Journal: Methods (San Diego, Calif.)
Published Date: Aug 6, 2024

Abstract

DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results.

Authors

  • Ke Han
    School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150040, China. hanke@hrbcu.edu.cn.
  • Jianchun Wang
    School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China.
  • Ying Chu
    School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China.
  • Qian Liao
    Department of Oral and Cranio-maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai, China.
  • Yijie Ding
    School of Computer Science and Technology, Tianjin University, Tianjin 300350, China. wuxi_dyj@tju.edu.cn.
  • Dequan Zheng
    MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, 150001, Harbin, PR China.
  • Jie Wan
    Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, 150001, China.
  • Xiaoyi Guo
    Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000 Wuxi, China.
  • Quan Zou

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