NucleicBERT interprets RNA sequence space through self-supervised language modelling

TL;DR - NucleicBERT is a self-supervised language model trained on large-scale RNA sequence data to learn biologically meaningful patterns without relying on extensive annotations. It could help researchers interpret RNA structure and function from abundant but sparsely annotated sequences.

• Addresses the scarcity of RNA annotations by learning directly from sequence correlations.
• Uses self-supervised language modelling across large-scale RNA data.
• Extracts biologically meaningful patterns from RNA sequence space.
• Aims to improve inference of RNA structure and function.