Imane Boudellioua, Maxat Kulmanov, Paul N. Schofield, Georgios V. Gkoutos and Robert Hoehndorf
BMC Bioinformatics (2019) 20:65
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 https://github.com/bio-ontology-research-group/ phenomenet-vp.
Conclusions DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
Health Data Research UK researchers develop innovative tools and technologies needed to unlock knowledge from complex and diverse health data, to address some of the biggest health challenges that...
Health Data Research UK (HDR UK) Midlands
Director Professor Simon Ball, Director of Digital Healthcare, University Hospital Birmingham Associate Directors Professor Theodoros Arvanitis Professor of e-health innovation and Head of...