A bidirectional LSTM-RNN and GRU method to exon prediction using splice-site mapping
Yükleniyor...
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Deep Learning techniques (DL) significantly improved the accuracy of predictions and classifications of deoxyribonucleic acid (DNA). On the other hand, identifying and predicting splice sites in eukaryotes is difficult due to many erroneous discoveries. To address this issue, we propose a deep learning model for recognizing and anticipating splice sites in eukaryotic DNA sequences based on a bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) and Gated recurrent unit (GRU). The non-coding introns of the gene are spliced out, and the coding exons are joined during the splicing of the original mRNA transcript. This bidirectional LSTM-RNN-GRU model incorporates intron features in order of their length constraints, beginning with splice site donor (GT) and ending with splice site acceptor (AG). The performance of the model improves as the number of training epochs grows. The best level of accuracy for this model is 96.1 percent.
Açıklama
Anahtar Kelimeler
Deep Learning, Exon, Intron, LSTM, Machine Learning, Splice Site
Kaynak
Applied Sciences (Switzerland)
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
12
Sayı
9
Künye
Canatalay, P. J., & Ucan, O. N. (2022). A bidirectional LSTM-RNN and GRU method to exon prediction using splice-site mapping. Applied Sciences, 12(9).
