Bayesian optimization.

<p>(A-C) Bayesian optimization in protein sequence-function prediction. % of top-100 scores in training set found for the CNN ensemble, CNN evidential, and GP methods evaluated on the AAV/Random (A), Meltome/Random (B), and GB1/Random (C) splits. The “greedy” strategy acquired sequences with t...

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Bibliographic Details
Main Author:	Kevin P. Greenman (17703253) (author)
Other Authors:	Ava P. Amini (15853140) (author), Kevin K. Yang (4528963) (author)
Published:	2025
Subjects:	Biochemistry Cell Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified select new sequences require calibrated estimations fitness landscape inference benchmarks enable us outperform greedy sampling metrics using one benchmarking uncertainty quantification retrospective active learning bayesian optimization settings bayesian optimization active learning based sampling property optimization results indicate results extend regression tasks rank correlation provide recommendations protein engineering often unable molecular machine model uncertainty model improvement learning datasets hot encoding function models effective design
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Bayesian optimization.

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