Re-ranker Models
LightGBM binary classifiers trained on temporal holdout data (CAFA protocol). A re-ranker uses alignment, taxonomy, and aggregate features to re-score GO predictions with calibrated probabilities, replacing the raw embedding distance ranking.
Train new re-ranker
No Knowledge: proteins with zero GO annotations at t0. Hardest setting — measures pure prediction ability.
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