DeepPhos: prediction of protein phosphorylation sites with deep learning.

Journal: Bioinformatics (Oxford, England)
Published Date: Aug 15, 2019

Abstract

MOTIVATION: Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.

Authors

  • Fenglin Luo
    School of Information Science and Technology.
  • Minghui Wang
    College of Chemistry and Material Science, Shandong Agricultural University, Tai'an 271018, PR China.
  • Yu Liu
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
  • Xing-Ming Zhao
    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
  • Ao Li
    Beijing University of Chinese Medicine, Beijing, China.

Keywords

External Resources

Popular Topics
Recent Journals