A bidirectional LSTM-RNN and GRU method to exon prediction using splice-site mapping

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dc.contributor.authorCanatalay, Peren Jerfi
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2022-06-01T11:18:10Z
dc.date.available2022-06-01T11:18:10Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractDeep 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.en_US
dc.identifier.citationCanatalay, P. J., & Ucan, O. N. (2022). A bidirectional LSTM-RNN and GRU method to exon prediction using splice-site mapping. Applied Sciences, 12(9).en_US
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85129787157
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2461
dc.identifier.volume12en_US
dc.identifier.wosWOS:000795386600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Osman Nuri
dc.language.isoen
dc.relation.ispartofApplied Sciences (Switzerland)
dc.relation.isversionof10.3390/app12094390en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep Learningen_US
dc.subjectExonen_US
dc.subjectIntronen_US
dc.subjectLSTMen_US
dc.subjectMachine Learningen_US
dc.subjectSplice Siteen_US
dc.titleA bidirectional LSTM-RNN and GRU method to exon prediction using splice-site mapping
dc.typeArticle

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