DEEPre: sequence-based enzyme EC number prediction by deep learning.

Journal: Bioinformatics (Oxford, England)
Published Date: Mar 1, 2018

Abstract

MOTIVATION: Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.

Authors

  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.
  • Sheng Wang
    Intensive Care Medical Center, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, People's Republic of China.
  • Ramzan Umarov
    Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), Computer, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
  • Bingqing Xie
    Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA Department of Computer Science, Illinois Institute of Technology, Chicago, IL 60616, USA.
  • Ming Fan
    Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Lihua Li
    College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address: lilh@hdu.edu.cn.
  • Xin Gao
    Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.

Keywords

External Resources

Popular Topics
Recent Journals