Splice-site identification for exon prediction using bidirectional LSTM-RNN approach.

ABSTRACT: Machine learning methods played a major role in improving the accuracy of predictions and classification of DNA (Deoxyribonucleic Acid) and protein sequences. In eukaryotes, Splice-site identification and prediction is though not a straightforward job because of numerous false positives. To solve this problem, here, in this paper, we represent a bidirectional Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) based deep learning model that has been developed to identify and predict the splice-sites for the prediction of exons from eukaryotic DNA sequences. During the splicing mechanism of the primary mRNA transcript, the introns, the non-coding region of the gene are spliced out and the exons, the coding region of the gene are joined. This bidirectional LSTM-RNN model uses the in

SUBMITTER: Singh N 

PROVIDER: S-EPMC9157471 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature