Spliceator: multi-species splice site prediction using convolutional neural networks

Background: Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking. Results: We develope...

Full description

Bibliographic Details
Main Authors:	Collet, P. (Author), Jeannin-Girardon, A. (Author), Kress, A. (Author), Moulinier, L. (Author), Orhand, R. (Author), Poch, O. (Author), Scalzitti, N. (Author), Thompson, J.D (Author), Weber, T. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	article controlled study Convolution convolutional neural network Convolutional neural network Convolutional neural networks data quality Data quality deep learning Deep learning fish Forecasting Genes Genome annotation High-accuracy human human experiment Learning algorithms Learning methods Model organisms Multi-species nonhuman Plants (botany) prediction protist Splice site Splice site prediction Splice Site Prediction
Online Access:	View Fulltext in Publisher

Internet

View Fulltext in Publisher

Spliceator: multi-species splice site prediction using convolutional neural networks

Internet

Similar Items