DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have been developed to recognize GSRs, their pure computational identification remains challenging. Moreover, various GSRs usually require a specialized set of features for developing robust recognition models. Recently, deep-learning (DL) methods have been shown to generate more accurate prediction models than 'shallow' methods without the need to develop specialized features for the problems in question. Here, we explore the potential use of DL for the recognition of GSRs.

Authors

  • Manal Kalkatawi
    Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
  • Arturo Magana-Mora
    King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal 23955-6900, Saudi Arabia; Saudi Aramco, EXPEC-ARC, Drilling Technology Team, Dhahran 31311, Saudi Arabia.
  • Boris Jankovic
    Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
  • Vladimir B Bajic
    King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia.