<scp> m ⁵ C </scp> ‐ <scp>TNKmer</scp> : Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques

ABSTRACT 5‐Methylcytosine (m 5 C) 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 m 5 C 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 m 5 C sites in RNA remain challenging tasks. Machine learning techniques offer an efficient alternative to traditional laboratory methods for identifying m 5 C sites in Homo sapiens . This study introduces a novel computational model m 5 C‐TNKmer, which utilizes k‐mer feature extraction to enhance the identification of m 5 C 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 m 5 C‐TNKmer achieved an impressive accuracy of 96.15%. This model provides a powerful tool for scientists to accurately identify RNA m 5 C sites, contributing to a deeper understanding of genetic functions and regulatory mechanisms.