DeepFam: deep learning based alignment-free method for protein family modeling and prediction.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: A large number of newly sequenced proteins are generated by the next-generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to characterize such a large number of protein sequences, thus protein function prediction is primarily done by computational modeling methods, such as profile Hidden Markov Model (pHMM) and k-mer based methods. Nevertheless, existing methods have some limitations; k-mer based methods are not accurate enough to assign protein functions and pHMM is not fast enough to handle large number of protein sequences from numerous genome projects. Therefore, a more accurate and faster protein function prediction method is needed.

Authors

  • Seokjun Seo
    Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.
  • Minsik Oh
    Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.
  • Youngjune Park
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
  • Sun Kim
    National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA. sun.kim@nih.gov.