Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach

Harun-Or-Roshid, Md.; Maeda, Kazuhiro; Phan, Le Thi; Manavalan, Balachandran; Kurata, Hiroyuki

doi:10.1016/j.compbiomed.2023.107848

Cited by 7 publications

(3 citation statements)

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“…The stacking approach represents an advanced ensemble method that enhances prediction performance by amalgamating the strengths of multiple models [45,64,[83][84][85][86][87]. Unlike conventional ensemble approaches that primarily use averaging and voting, stacking employs a PLOS ONE meta-model to adeptly combine the forecasts from its base models.…”

Section: Impact Of Meta-learning On the Development Of Meta-2ommentioning

confidence: 99%

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2′-O-methylation sites in human RNA

Harun-Or-Roshid,

Pham,

Manavalan

et al. 2024

PLoS ONE

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2′-O-methylation (2-OM or Nm) is a widespread RNA modification observed in various RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which plays a crucial role in several biological functional mechanisms and innate immunity. To comprehend its modification mechanisms and potential epigenetic regulation, it is necessary to accurately identify 2-OM sites. However, biological experiments can be tedious, time-consuming, and expensive. Furthermore, currently available computational methods face challenges due to inadequate datasets and limited classification capabilities. To address these challenges, we proposed Meta-2OM, a cutting-edge predictor that can accurately identify 2-OM sites in human RNA. In brief, we applied a meta-learning approach that considered eight conventional machine learning algorithms, including tree-based classifiers and decision boundary-based classifiers, and eighteen different feature encoding algorithms that cover physicochemical, compositional, position-specific and natural language processing information. The predicted probabilities of 2-OM sites from the baseline models are then combined and trained using logistic regression to generate the final prediction. Consequently, Meta-2OM achieved excellent performance in both 5-fold cross-validation training and independent testing, outperforming all existing state-of-the-art methods. Specifically, on the independent test set, Meta-2OM achieved an overall accuracy of 0.870, sensitivity of 0.836, specificity of 0.904, and Matthew’s correlation coefficient of 0.743. To facilitate its use, a user-friendly web server and standalone program have been developed and freely available at http://kurata35.bio.kyutech.ac.jp/Meta-2OM and https://github.com/kuratahiroyuki/Meta-2OM.

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