Machine-learning techniques for macromolecular crystallization data

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Abstract
Systematizing belief systems regarding macromolecular crystallization has two major advantages: automation and clarification. In this paper, methodologies are presented for systematizing and representing knowledge about the chemical and physical properties of additives used in crystallization experiments. A novel autonomous discovery program is introduced as a method to prune rule-based models produced from crystallization data augmented with such knowledge. Computational experiments indicate that such a system can retain and present informative rules pertaining to protein crystallization that warrant further confirmation via experimental techniques.

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