NucleicBERT interprets RNA sequence space through self-supervised language modelling
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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.
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NucleicBERT interprets RNA sequence space through self-supervised language modelling
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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.