BERT-Kcr: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: As one of the most important post-translational modifications (PTMs), protein lysine crotonylation (Kcr) has attracted wide attention, which involves in important physiological activities, such as cell differentiation and metabolism. However, experimental methods are expensive and time-consuming for Kcr identification. Instead, computational methods can predict Kcr sites in silico with high efficiency and low cost.

Authors

  • Yanhua Qiao
    College of Public Health, Affiliated Hospital of Hebei University, Hebei University, Baoding, 071000, Hebei, People's Republic of China. jab@hbu.edu.cn.
  • Xiaolei Zhu
    School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, China.
  • Haipeng Gong
    School of Life Science, Tsinghua University, Beijing 100084, China.