Name
Title | ||
|---|---|---|
Predicting The Oncogenic Potential Of Gene Fusions Using Convolutional Neural Networks |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-34585-3_24 | COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, CIBB 2018 |
Keywords | Field | DocType |
Gene fusions, Deep learning, Convolutional Neural Networks | Gene,Computer science,Convolutional neural network,Artificial intelligence,Deep learning,Machine learning | Conference |
Volume | ISSN | Citations |
11925 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marta Lovino | 1 | 0 | 0.68 |
| Gianvito Urgese | 2 | 21 | 9.52 |
| Enrico Macii | 3 | 2405 | 349.96 |
| Santa Di Cataldo | 4 | 76 | 10.82 |
| Elisa Ficarra | 5 | 122 | 22.25 |