iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural Network
DNA N6-methyladenine (6mA) is related to a vast range of biological progress like transcription, replication, and repair of DNA. The precise discrimination of the 6mA sites plays a vital role in the understanding of its biological functions. Even though biochemical experiments produced positive resu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8930537/ |
| _version_ | 1819174313980329984 |
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| author | Abdul Wahab Syed Danish Ali Hilal Tayara Kil To Chong |
| author_facet | Abdul Wahab Syed Danish Ali Hilal Tayara Kil To Chong |
| author_sort | Abdul Wahab |
| collection | DOAJ |
| description | DNA N6-methyladenine (6mA) is related to a vast range of biological progress like transcription, replication, and repair of DNA. The precise discrimination of the 6mA sites plays a vital role in the understanding of its biological functions. Even though biochemical experiments produced positive results, they were inefficient in terms of cost and time. Therefore, to facilitate the identification of 6mA sites it is important to develop a robust computational model. In this regard, we develop a deep learning-based computational model named as iIM-CNN for the identification of N6-methyladenine sites from DNA sequences. The iIM-CNN is capable of extracting important features using a convolution neural network (CNN). The proposed model achieves the Mathew correlation coefficient (MCC) of 0.651, 0.752 and 0.941 for cross-species, Rice, and M. musculus genome respectively. The comparison of the outcomes depicts that the proposed model outperforms the existing computational tools for the prediction of the 6mA sites. |
| first_indexed | 2024-12-22T20:37:00Z |
| format | Article |
| id | doaj.art-1522ee84c2aa4659868e850b3b8f1fa9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:37:00Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1522ee84c2aa4659868e850b3b8f1fa92022-12-21T18:13:26ZengIEEEIEEE Access2169-35362019-01-01717857717858310.1109/ACCESS.2019.29586188930537iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural NetworkAbdul Wahab0https://orcid.org/0000-0003-4531-5252Syed Danish Ali1https://orcid.org/0000-0001-5204-5073Hilal Tayara2https://orcid.org/0000-0001-5678-3479Kil To Chong3https://orcid.org/0000-0002-1952-0001Department of Electronics and Information Engineering, Chonbuk National University, Jeonju, South KoreaDepartment of Electronics and Information Engineering, Chonbuk National University, Jeonju, South KoreaDepartment of Electronics and Information Engineering, Chonbuk National University, Jeonju, South KoreaAdvanced Electronics and Information Research Center, Chonbuk National University, Jeonju, South KoreaDNA N6-methyladenine (6mA) is related to a vast range of biological progress like transcription, replication, and repair of DNA. The precise discrimination of the 6mA sites plays a vital role in the understanding of its biological functions. Even though biochemical experiments produced positive results, they were inefficient in terms of cost and time. Therefore, to facilitate the identification of 6mA sites it is important to develop a robust computational model. In this regard, we develop a deep learning-based computational model named as iIM-CNN for the identification of N6-methyladenine sites from DNA sequences. The iIM-CNN is capable of extracting important features using a convolution neural network (CNN). The proposed model achieves the Mathew correlation coefficient (MCC) of 0.651, 0.752 and 0.941 for cross-species, Rice, and M. musculus genome respectively. The comparison of the outcomes depicts that the proposed model outperforms the existing computational tools for the prediction of the 6mA sites.https://ieeexplore.ieee.org/document/8930537/DNA N6-methyladeninesequence analysiscross-speciesdeep learningconvolution neural network |
| spellingShingle | Abdul Wahab Syed Danish Ali Hilal Tayara Kil To Chong iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural Network IEEE Access DNA N6-methyladenine sequence analysis cross-species deep learning convolution neural network |
| title | iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural Network |
| title_full | iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural Network |
| title_fullStr | iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural Network |
| title_full_unstemmed | iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural Network |
| title_short | iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural Network |
| title_sort | iim cnn intelligent identifier of 6ma sites on different species by using convolution neural network |
| topic | DNA N6-methyladenine sequence analysis cross-species deep learning convolution neural network |
| url | https://ieeexplore.ieee.org/document/8930537/ |
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