A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping
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 learn...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4390 |
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| author | Peren Jerfi CANATALAY Osman Nuri Ucan |
| author_facet | Peren Jerfi CANATALAY Osman Nuri Ucan |
| author_sort | Peren Jerfi CANATALAY |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:22:25Z |
| format | Article |
| id | doaj.art-f0ceb46c4f1a4ff5bf5d530de5e96358 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:22:25Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f0ceb46c4f1a4ff5bf5d530de5e963582023-11-23T07:48:23ZengMDPI AGApplied Sciences2076-34172022-04-01129439010.3390/app12094390A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site MappingPeren Jerfi CANATALAY0Osman Nuri Ucan1Faculty of Engineering, Computer Science, Altinbas University, 34217 Istanbul, TurkeyFaculty of Engineering, Computer Science, Altinbas University, 34217 Istanbul, TurkeyDeep 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.https://www.mdpi.com/2076-3417/12/9/4390splice siteintronexonmachine learningdeep learningLSTM |
| spellingShingle | Peren Jerfi CANATALAY Osman Nuri Ucan A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping Applied Sciences splice site intron exon machine learning deep learning LSTM |
| title | A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping |
| title_full | A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping |
| title_fullStr | A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping |
| title_full_unstemmed | A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping |
| title_short | A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping |
| title_sort | bidirectional lstm rnn and gru method to exon prediction using splice site mapping |
| topic | splice site intron exon machine learning deep learning LSTM |
| url | https://www.mdpi.com/2076-3417/12/9/4390 |
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