NucleicBERT interprets RNA sequence space through self-supervised language modelling
Nature Machine Intelligence, Published online: 03 September 2026; doi:10.1038/s42256-026-01295-9 RNA structure and function are hard to infer because annotations are scarce, despite abundant sequence data. Upadhyay et al. trained a self-supervised model on large-scale RNA data that derives biologically meaningful patterns from sequence correlations.
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