PathoRM: Computational inference of pathogenic RNA methylation sites by incorporating multi-view features

Identifying pathogenic RNA methylation sites with a reasonable biological explanation has important implications for the treatment of diseases. Due to the limitations of in vitro experiments in identifying pathogenic RNA methylation sites, there is a growing need for computational workflows to enable accurate inference. Here, motivated by this profound meaning, we developed PathoRM, a biologically informed deep learning model, to infer associations between RNA methylation sites and diseases. PathoRM could provide convincing pathogenic RNA methylation sites and unravel the enigma of pathology in the epi-transcriptomic layer. PathoRM fuses RNA methylation host sequences and pathogenic descriptions as inputs, and subsequently employs large language models, multi-view learning algorithm, graph neural networks, an adversarial training approach, and "guilty-by-association"-derived negative sampling approach. PathoRM distils the semantically enriched feature embeddings, leading to more accurate and robust prediction performance across the metrics and datasets. Notably, incorporated with attention mechanism, PathoRM bestows itself biological interpretability through illuminating the dark matters in the host sequences of RNA methylation sites. This work is expected to assist in the discovery of pathogenic RNA methylation sites and conserved motifs, contributing to the advancement of genome research. Codes and pre-trained model are accessible at https://github.com/jianiM/PathoRM.