DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool
DNA N<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mn>6</mn></msup></semantics></math></inline-formula>-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA rep...
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doaj-ca438195434444b1aece754039150e1c2020-11-25T03:48:29ZengMDPI AGGenes2073-44252020-08-011189889810.3390/genes11080898DNA6mA-MINT: DNA-6mA Modification Identification Neural ToolMobeen Ur Rehman0Kil To Chong1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaDNA N<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mn>6</mn></msup></semantics></math></inline-formula>-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on <i>Mus musculus</i>, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.https://www.mdpi.com/2073-4425/11/8/898DNA N6-methyladenineChou’s 5-steps ruleConvolution Neural Network (CNN)Long Short-Term Memory (LSTM)computational biology |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mobeen Ur Rehman Kil To Chong |
spellingShingle |
Mobeen Ur Rehman Kil To Chong DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool Genes DNA N6-methyladenine Chou’s 5-steps rule Convolution Neural Network (CNN) Long Short-Term Memory (LSTM) computational biology |
author_facet |
Mobeen Ur Rehman Kil To Chong |
author_sort |
Mobeen Ur Rehman |
title |
DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool |
title_short |
DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool |
title_full |
DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool |
title_fullStr |
DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool |
title_full_unstemmed |
DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool |
title_sort |
dna6ma-mint: dna-6ma modification identification neural tool |
publisher |
MDPI AG |
series |
Genes |
issn |
2073-4425 |
publishDate |
2020-08-01 |
description |
DNA N<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mn>6</mn></msup></semantics></math></inline-formula>-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on <i>Mus musculus</i>, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely. |
topic |
DNA N6-methyladenine Chou’s 5-steps rule Convolution Neural Network (CNN) Long Short-Term Memory (LSTM) computational biology |
url |
https://www.mdpi.com/2073-4425/11/8/898 |
work_keys_str_mv |
AT mobeenurrehman dna6mamintdna6mamodificationidentificationneuraltool AT kiltochong dna6mamintdna6mamodificationidentificationneuraltool |
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1724498810084786176 |