DHUpredET: A comparative computational approach for identification of dihydrouridine modification sites in RNA sequence

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Laboratory-based detection of D sites is laborious and expensive. In this study, we developed effective machine learning models employing efficient feature encoding methods to identify D sites. Initially, we explored various state-of-the-art feature encoding approaches and 30 machine learning techniques for each and selected the top eight models based on their independent testing and cross-validation outcomes. Finally, we developed DHUpredET using the extra tree classifier methods for predicting DHU sites. The DHUpredET model demonstrated balanced performance across all evaluation criteria, outperforming state-of-the-art models by 8% and 14% in terms of accuracy and sensitivity, respectively, on an independent test set. Further analysis revealed that the model achieved higher accuracy with position-specific two nucleotide (PS2) features, leading us to conclude that PS2 features are the best suited for the DHUpredET model. Therefore, our proposed model emerges as the most favorite choice for predicting D sites. In addition, we conducted an in-depth analysis of local features and identified a particularly significant attribute with a feature score of 0.035 for PS2_299 attributes. This tool holds immense promise as an advantageous instrument for accelerating the discovery of D modification sites, which contributes too many targeting therapeutic and understanding RNA structure. • Executed an in-depth analysis employing various machine learning techniques. • Uncovered the most informative features and optimal machine-learning algorithms for predicting D sites in RNA sequences. • Proposed DHUpredET, a machine learning-based method than can significantly outperform previous studies found in the literature. • Exceled at leveraging position-specific two nucleotides (PS2) features with appropriate execution. • Identified the positive class with greater than 85% and specified the negative class with more than 82% accuracy, which produces a balance outcome.

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