Show simple item record

dc.contributor.authorBoudellioua, Imane
dc.contributor.authorKulmanov, Maxat
dc.contributor.authorSchofield, Paul N.
dc.contributor.authorGkoutos, Georgios V.
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2019-02-07T07:02:43Z
dc.date.available2019-02-07T07:02:43Z
dc.date.issued2019-02-06
dc.identifier.citationBMC Bioinformatics. 2019 Feb 06;20(1):65
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/288868
dc.description.abstractAbstract 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.
dc.titleDeepPVP: phenotype-based prioritization of causative variants using deep learning
dc.typeJournal Article
dc.date.updated2019-02-07T07:02:35Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dc.identifier.doi10.17863/CAM.36133
rioxxterms.versionofrecord10.1186/s12859-019-2633-8


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record