DeepPVP: phenotype-based prioritization of causative variants using deep learning.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype.

Authors

  • Imane Boudellioua
    Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia.
  • Maxat Kulmanov
    Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia.
  • Paul N Schofield
    Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK. pns12@cam.ac.uk.
  • Georgios V Gkoutos
    Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom; Institute of Translational Medicine, University of Birmingham, Birmingham, United Kingdom; NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, United Kingdom; MRC Health Data Research UK (HDR UK), London, United Kingdom; NIHR Experimental Cancer Medicine Centre, Birmingham, United Kingdom; NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham, United Kingdom.
  • 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.