Active learning.
<p>(A) Schematic of active learning approach. A model is trained on an initial dataset, and is then retrained in each iteration by adding more points to the training set based on some selection criteria. (B-D) Uncertainty-guided active learning in protein sequence-function prediction. Spearman...
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2025
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| Summary: | <p>(A) Schematic of active learning approach. A model is trained on an initial dataset, and is then retrained in each iteration by adding more points to the training set based on some selection criteria. (B-D) Uncertainty-guided active learning in protein sequence-function prediction. Spearman rank correlation of predictions (<i>ρ</i>) for the CNN ensemble, CNN evidential, and GP methods evaluated on the AAV/Random (B), Meltome/Random (C), and GB1/Random (D) splits. The “random” strategy acquired sequences with all unseen points having equal probabilities, the “explorative sample” strategy acquired sequences with random sampling weighted by uncertainty, and the “explorative greedy” strategy acquired the previously unseen sequences with the highest uncertainty. See the Uncertainty Methods section for an explanation of why GP experiments for the AAV landscape were not feasible.</p> |
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