Machine-learning Techniques for Macromolecular Crystallization Data

Gopalakrishnan, V., Livingston, G., Hennessy, D., Buchanan, B., Rosenberg, J.M. Machine-Learning Techniques for Macromolecular Crystallization Data, Acta Crystallogr D Biol Crystallogr 60 (2004)1705-1716.  PMID:15388916

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.

Publication Year: 
2004
Publication Credits: 
Gopalakrishnan, V., Livingston, G., Hennessy, D., Buchanan, B., Rosenberg, J.M.
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