DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features
In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living th...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/5483115 |
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| author | Omar Barukab Yaser Daanial Khan Sher Afzal Khan Kuo-Chen Chou |
| author_facet | Omar Barukab Yaser Daanial Khan Sher Afzal Khan Kuo-Chen Chou |
| author_sort | Omar Barukab |
| collection | DOAJ |
| description | In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins. |
| format | Article |
| id | doaj-art-9f277db93e004dd0b86985652d3cc1fa |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-9f277db93e004dd0b86985652d3cc1fa2025-02-03T01:23:14ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/5483115DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based FeaturesOmar Barukab0Yaser Daanial Khan1Sher Afzal Khan2Kuo-Chen Chou3Department of Information TechnologyDepartment of Computer ScienceDepartment of Computer SciencesGordon Life Science InstituteIn the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.http://dx.doi.org/10.1155/2022/5483115 |
| spellingShingle | Omar Barukab Yaser Daanial Khan Sher Afzal Khan Kuo-Chen Chou DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features Applied Bionics and Biomechanics |
| title | DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features |
| title_full | DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features |
| title_fullStr | DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features |
| title_full_unstemmed | DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features |
| title_short | DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features |
| title_sort | dnapred prot identification of dna binding proteins using composition and position based features |
| url | http://dx.doi.org/10.1155/2022/5483115 |
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