DEEPrior: a deep learning tool for the prioritization of gene fusions.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
01 05 2020
Historique:
received: 25 10 2019
revised: 20 01 2020
accepted: 28 01 2020
pubmed: 6 2 2020
medline: 30 10 2020
entrez: 5 2 2020
Statut: ppublish

Résumé

In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user. Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 32016382
pii: 5722203
doi: 10.1093/bioinformatics/btaa069
pmc: PMC7214024
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3248-3250

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press.

Références

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Auteurs

Marta Lovino (M)

Department of Control and Computer Engineering.

Maria Serena Ciaburri (MS)

Department of Control and Computer Engineering.

Gianvito Urgese (G)

Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Torino 10129, Italy.

Santa Di Cataldo (S)

Department of Control and Computer Engineering.

Elisa Ficarra (E)

Department of Control and Computer Engineering.

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Classifications MeSH