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Bayesian active learning for optimization and Uncertainty quantification in protein docking()
Ab initio protein docking represents a major challenge for optimizing a noisy and costly “black box”-like function in a high-dimensional space. Despite progress in this field, there is a lack of rigorous uncertainty quantification (UQ). To fill the gap, we introduce a novel algorithm, Bayesian Activ...
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| Gepubliceerd in: | J Chem Theory Comput |
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| Hoofdauteurs: | , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
2020
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7429362/ https://ncbi.nlm.nih.gov/pubmed/32558561 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1021/acs.jctc.0c00476 |
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