Acta Cryst. (2004). D60, 1705-1716 [ doi:10.1107/S090744490401683X ]
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.
Keywords: protein crystallization; machine learning; hierarchical data representation; data mining; automatic discovery; heuristic rules.
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