Predicting the oncogenic potential of a gene fusion transcript is an important and challenging task in the study of cancer development. To this date, the available approaches mostly rely on protein domain analysis to provide a probability score explaining the oncogenic potential of a gene fusion. In this paper, a Convolutional Neural Network model is proposed to discriminate gene fusions into oncogenic or non-oncogenic, exploiting only the protein sequence without protein domain information. Our proposed model obtained accuracy value close to 90% on a dataset of fused sequences.

Predicting the oncogenic potential of gene fusions using convolutional neural networks / Lovino, Marta; Urgese, Gianvito; Macii, Enrico; DI CATALDO, Santa; Ficarra, Elisa (LECTURE NOTES IN COMPUTER SCIENCE). - In: Computational Intelligence Methods for Bioinformatics and BiostatisticsELETTRONICO. - [s.l] : Springer International Publishing, 2020. - ISBN 978-3-030-34584-6. - pp. 277-284 [10.1007/978-3-030-34585-3_24]

Predicting the oncogenic potential of gene fusions using convolutional neural networks

LOVINO, MARTA;Gianvito Urgese;Enrico Macii;Santa Di Cataldo;Elisa Ficarra
2020

Abstract

Predicting the oncogenic potential of a gene fusion transcript is an important and challenging task in the study of cancer development. To this date, the available approaches mostly rely on protein domain analysis to provide a probability score explaining the oncogenic potential of a gene fusion. In this paper, a Convolutional Neural Network model is proposed to discriminate gene fusions into oncogenic or non-oncogenic, exploiting only the protein sequence without protein domain information. Our proposed model obtained accuracy value close to 90% on a dataset of fused sequences.
2020
978-3-030-34584-6
978-3-030-34585-3
Computational Intelligence Methods for Bioinformatics and Biostatistics
File in questo prodotto:
File Dimensione Formato  
10.1007_978-3-030-34585-3.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.21 MB
Formato Adobe PDF
1.21 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Fusioni_Springer_LNBI (1).pdf

Open Access dal 24/01/2021

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 361.59 kB
Formato Adobe PDF
361.59 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2712598