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.
CITATION STYLE
Lovino, M., Urgese, G., Macii, E., di Cataldo, S., & Ficarra, E. (2020). Predicting the Oncogenic Potential of Gene Fusions Using Convolutional Neural Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11925 LNBI, pp. 277–284). Springer. https://doi.org/10.1007/978-3-030-34585-3_24
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