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EnglishSummary: 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.
DEEPrior: a deep learning tool for the prioritization of gene fusions / Lovino, Marta; Ciaburri, Maria Serena; Urgese, Gianvito; Di Cataldo, Santa; Ficarra, Elisa. - In: BIOINFORMATICS. - ISSN 1367-4803. - ELETTRONICO. - (2020). [10.1093/bioinformatics/btaa069]
| Titolo: | DEEPrior: a deep learning tool for the prioritization of gene fusions | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
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| Digital Object Identifier (DOI): | http://dx.doi.org/10.1093/bioinformatics/btaa069 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| btaa069.pdf | Articolo principale | 2. Post-print / Author's Accepted Manuscript |  | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2796722
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