title: Machine-learning techniques for macromolecular crystallization data.
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.
authors.affiliation: Intelligent Systems Laboratory, University of Pittsburgh, Pittsburgh, PA 15260, USA.
authors.forename: Vanathi
authors.initials: V
authors.lastname: Gopalakrishnan
authors.forename: Gary
authors.initials: G
authors.lastname: Livingston
authors.forename: Daniel
authors.initials: D
authors.lastname: Hennessy
authors.forename: Bruce
authors.initials: B
authors.lastname: Buchanan
authors.forename: John M
authors.initials: JM
authors.lastname: Rosenberg
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003460:Crystallization / Q000379:methods*; D018360:Crystallography, X-Ray / Q000379:methods*; D046912:Multiprotein Complexes; D036103:Nanotechnology; D016571:Neural Networks, Computer; D012984:Software
chemical_list: D046912:Multiprotein Complexes