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.
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. - 36:10(2020), pp. 3248-3250. [10.1093/bioinformatics/btaa069]
DEEPrior: a deep learning tool for the prioritization of gene fusions
Lovino, Marta;Ciaburri, Maria Serena;Urgese, Gianvito;Di Cataldo, Santa;Ficarra, Elisa
2020
Abstract
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.File | Dimensione | Formato | |
---|---|---|---|
bioinformatics_36_10_3248.pdf
accesso aperto
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Creative commons
Dimensione
220.82 kB
Formato
Adobe PDF
|
220.82 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2796722