DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem.

Authors

  • Maxat Kulmanov
    Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia.
  • Mohammed Asif Khan
    Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.
  • Robert Hoehndorf
    Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia. robert.hoehndorf@kaust.edu.sa.
  • Jonathan Wren