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

SUMMARY: 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...

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Detalles Bibliográficos
Autores principales: Lovino, Marta, Ciaburri, Maria Serena, Urgese, Gianvito, Di Cataldo, Santa, Ficarra, Elisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214024/
https://www.ncbi.nlm.nih.gov/pubmed/32016382
http://dx.doi.org/10.1093/bioinformatics/btaa069
Descripción
Sumario:SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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 INFORMATION: Supplementary data are available at Bioinformatics online.