Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry
Technical Report,01 Jul 2012,30 Sep 2015
TRUSTEES OF PRINCETON UNIVERSITY Princeton United States
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We have extended our work in optimal learning for continuous parameters, which previously was limited to searching among a small set of possible sample values, to amore general resampling approach that allows us to quickly find the best parameter values globally. We also developed a version of Peptide Optimization with Optimal Learning POOL that can be used with quantitative responses, rather than the binary responses assumed by the first version of the method. This greatly expands the set of applications to which POOL can be applied. We also developed a statistical method for characterizing the bias in phage display, and making predictions of activity based on phage display data. This method can be used on its own, and can also provide input to POOL. Finally, we have developed a series of tutorial materials, first in the form of a series of powerpoint presentations, and second as a book chapter, both geared toward materials scientists.