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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–3 of 3 filtered models
Graph-attention model that predicts A-to-I RNA editing from sequence and secondary structure, treating RNA as a graph with base-pairing edges.
Structure-aware transformer that makes zero-shot, per-adenosine predictions of ADAR-mediated A-to-I RNA editing to guide therapeutic guide-RNA design.
RNA foundation model trained on chemical-mapping data from millions of sequences, predicting reactivity, secondary structure, and degradation.