Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications.

ABSTRACT: RNA plays a central role in diverse cellular functions across organisms, making the development of computational methods for RNA sequence analysis highly valuable. Recently, pre-trained genomic language models (gLMs) have emerged, offering enhanced flexibility for a range of downstream prediction tasks; however, comprehensive, unbiased evaluations remain limited. In this study, we benchmark eleven gLMs, alongside task-specific methods as comparison, across four RNA processes: non-coding RNA classification, N6-methyladenosine (m6A) modification prediction, alternative splice site prediction, and translation efficiency prediction. Rather than relying solely on increased data volume and model scale, our benchmark analysis demonstrates that outstanding performance arises from synergizing data

SUBMITTER: You N 

PROVIDER: S-EPMC12780105 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature