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

Research output: Contribution to journalArticlepeer-review


  • Imane Boudellioua
  • Maxat Kulmanov
  • Paul N. Schofield
  • Georgios Gkoutos
  • Robert Hoehndorf

Colleges, School and Institutes

External organisations

  • King Abdullah University of Science and Technology
  • University of Cambridge
  • University Hospitals Birmingham NHS Foundation Trust
  • NIHR Experimental Cancer Medicine Centre
  • NIHR Surgical Reconstruction and Microbiology Research Centre
  • NIHR Biomedical Research Centre
  • MRC Health Data Research UK


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.

Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at

Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.


Original languageEnglish
Article number65
Number of pages8
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 6 Feb 2019


  • Machine learning, Ontology, Phenotype, Variant prioritization