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 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; Gianvito, Urgese; Enrico, Macii; Santa Di Cataldo, ; Ficarra, Elisa. - 11925:(2020), pp. 277-284. (Intervento presentato al convegno 15th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018 tenutosi a Caparica nel 6 - 8 September 2018) [10.1007/978-3-030-34585-3_24].
Predicting the oncogenic potential of gene fusions using convolutional neural networks
LOVINO, MARTA;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.
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