m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques
ABSTRACT 5‐Methylcytosine (m5C) is a widely recognized epigenetic modification in ribonucleic acid (RNA), catalyzed by methyltransferases. This modification is crucial for various biological functions. While the role of m5C in deoxyribonucleic acid (DNA) has been extensively studied, its role in RNA...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.13073 |
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| author | Shahid Qazi Dilawar Shah Mohammad Asmat Ullah Khan Shujaat Ali Mohammad Abrar Asfandyar Khan Muhammad Tahir |
| author_facet | Shahid Qazi Dilawar Shah Mohammad Asmat Ullah Khan Shujaat Ali Mohammad Abrar Asfandyar Khan Muhammad Tahir |
| author_sort | Shahid Qazi |
| collection | DOAJ |
| description | ABSTRACT 5‐Methylcytosine (m5C) is a widely recognized epigenetic modification in ribonucleic acid (RNA), catalyzed by methyltransferases. This modification is crucial for various biological functions. While the role of m5C in deoxyribonucleic acid (DNA) has been extensively studied, its role in RNA is still in its early stages of exploration. Accurate and systematic detection and classification of m5C sites in RNA remain challenging tasks. Machine learning techniques offer an efficient alternative to traditional laboratory methods for identifying m5C sites in Homo sapiens. This study introduces a novel computational model m5C‐TNKmer, which utilizes k‐mer feature extraction to enhance the identification of m5C sites in RNA sequences. Four sub‐datasets derived from the primary dataset Di‐nucleotide (DNC), Tri‐nucleotide (TNC), Tetra‐nucleotide (Tetra‐NC), and Penta‐nucleotide (Penta‐NC) were used to train the model. The results demonstrated that m5C‐TNKmer achieved an impressive accuracy of 96.15%. This model provides a powerful tool for scientists to accurately identify RNA m5C sites, contributing to a deeper understanding of genetic functions and regulatory mechanisms. |
| format | Article |
| id | doaj-art-420a32affacd4aba9eda38d9d36c8d41 |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-420a32affacd4aba9eda38d9d36c8d412025-01-31T00:22:49ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.13073m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning TechniquesShahid Qazi0Dilawar Shah1Mohammad Asmat Ullah Khan2Shujaat Ali3Mohammad Abrar4Asfandyar Khan5Muhammad Tahir6Department of Computer Science Bacha Khan University Charsadda Khyber Pakhtunkhwa PakistanDepartment of Computer Science Bacha Khan University Charsadda Khyber Pakhtunkhwa PakistanDepartment of Computer Science, Faculty of Computing and Information Technology International Islamic University Islamabad PakistanDepartment of Computer Science Bacha Khan University Charsadda Khyber Pakhtunkhwa PakistanFaculty of Computer Studies Arab Open University Muscat Sultanate of OmanDepartment of Computer Science and Information Technology Hazara University Mansehra PakistanDepartment of Computer Science Kardan University Kabul AfghanistanABSTRACT 5‐Methylcytosine (m5C) is a widely recognized epigenetic modification in ribonucleic acid (RNA), catalyzed by methyltransferases. This modification is crucial for various biological functions. While the role of m5C in deoxyribonucleic acid (DNA) has been extensively studied, its role in RNA is still in its early stages of exploration. Accurate and systematic detection and classification of m5C sites in RNA remain challenging tasks. Machine learning techniques offer an efficient alternative to traditional laboratory methods for identifying m5C sites in Homo sapiens. This study introduces a novel computational model m5C‐TNKmer, which utilizes k‐mer feature extraction to enhance the identification of m5C sites in RNA sequences. Four sub‐datasets derived from the primary dataset Di‐nucleotide (DNC), Tri‐nucleotide (TNC), Tetra‐nucleotide (Tetra‐NC), and Penta‐nucleotide (Penta‐NC) were used to train the model. The results demonstrated that m5C‐TNKmer achieved an impressive accuracy of 96.15%. This model provides a powerful tool for scientists to accurately identify RNA m5C sites, contributing to a deeper understanding of genetic functions and regulatory mechanisms.https://doi.org/10.1002/eng2.13073DNCk‐mersmethylcytosinePenta‐NCRFRNA |
| spellingShingle | Shahid Qazi Dilawar Shah Mohammad Asmat Ullah Khan Shujaat Ali Mohammad Abrar Asfandyar Khan Muhammad Tahir m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques Engineering Reports DNC k‐mers methylcytosine Penta‐NC RF RNA |
| title | m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques |
| title_full | m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques |
| title_fullStr | m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques |
| title_full_unstemmed | m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques |
| title_short | m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques |
| title_sort | m5c tnkmer identification of 5 methylated base cytosine of ribonucleic acid using supervised machine learning techniques |
| topic | DNC k‐mers methylcytosine Penta‐NC RF RNA |
| url | https://doi.org/10.1002/eng2.13073 |
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