Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 22, 2015
- Accession Number
- AD1001807
Entities
People
- Peter Frazier
- Warren B. Powell
Organizations
- Trustees of Princeton University