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

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.