HD-6mAPred: a hybrid deep learning approach for accurate prediction of N6-methyladenine sites in plant species.

Journal: PeerJ
Published Date:

Abstract

BACKGROUND: N6-methyladenine (6mA) is an important DNA methylation modification that serves a crucial function in various biological activities. Accurate prediction of 6mA sites is essential for elucidating its biological function and underlying mechanism. Although existing methods have achieved great success, there remains a pressing need for improved prediction accuracy and generalization cap ability across diverse species. This study aimed to develop a robust method to address these challenges.

Authors

  • Huimin Li
    a Department of Pharmacy , Special Drugs R&D Center of People's Armed Police Forces , Logistics University of Chinese People's Armed Police Forces , Tianjin , China.
  • Wei Gao
    Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.
  • Yi Tang
    Department of Nephrology, Institute of Nephrology, West China Hospital of Sichuan University, Chengdu, China.
  • Xiaotian Guo
    School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan, China.