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November 24, 2025BMC BioinformaticsOpen Access

OMetaNet: an efficient hybrid deep learning model based on multimodal data fusion and contrastive learning for predicting 2ʹ-O-methylation sites in human RNA

Peng Shen Sen Yang Ziding Zhang

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Abstract

We proposed a novel computational model, OMetaNet. Its unique design structure may potentially reshape the paradigm of transcriptome analysis, open up new directions for extracting modification site information, and show significant potential in biomarker research and cross-species generalization studies.

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Cite This Study

Shen et al. (2025) studied this question.

synapsesocial.com/papers/69403ba12d562116f290cc02 https://doi.org/10.1186/s12859-025-06324-9

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1OMetaNet: an efficient hybrid deep learning model based on multimodal data fusion and contrastive learning for predicting 2ʹ-O-methylation sites in human RNA2025
2Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2′-O-methylation sites in human RNA2024 · 10 citations
3An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2′-O-Methylation SitesOriginal Research · 2026
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5TMolNet: A Task-Aware Multimodal Neural Network for Molecular Property Prediction2025

Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets2024
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