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

ABSTRACT: 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

SUBMITTER: Liu H 

PROVIDER: S-EPMC12617926 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature