Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

Journal: Bioinformatics (Oxford, England)
Published Date: Mar 1, 2017

Abstract

MOTIVATION: Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction.

Authors

  • Jack Hanson
    Signal Processing Laboratory, Griffith University, Brisbane 4111, Australia.
  • Yuedong Yang
    Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.
  • Kuldip Paliwal
    Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.
  • Yaoqi Zhou
    Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong, 518106, China. Electronic address: zhouyq@szbl.ac.cn.

Keywords

External Resources

Popular Topics
Recent Journals