MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

Journal: Bioinformatics (Oxford, England)
Published Date: Dec 15, 2017

Abstract

MOTIVATION: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction.

Authors

  • Duolin Wang
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Shuai Zeng
    Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
  • Chunhui Xu
    Informatics Institute, University of Missouri, Columbia, MO 65201, USA.
  • Wangren Qiu
    Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333046, China.
  • Yanchun Liang
    * College of Computer Science and Technology, Key Laboratory of Symbolic, Computation and Knowledge, Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China.
  • Trupti Joshi
    Department of Electrical Engineering and Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

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