title: Improved classification of crystallization images using data fusion and multiple classifiers.
abstract: Identifying the conditions that will produce diffraction-quality crystals can require very many crystallization experiments. The use of robots has increased the number of experiments performed in most laboratories, while in structural genomics centres tens of thousands of experiments can be produced every day. Reliable automated evaluation of these experiments is becoming increasingly important. A more robust classification is achieved by combining different methods of feature extraction with the use of multiple classifiers.
authors.affiliation: Departments of Mathematics and Chemistry, University of York, York YO10 5YW, England.
authors.forename: Samarasena
authors.initials: S
authors.lastname: Buchala
authors.forename: Julie C
authors.initials: JC
authors.lastname: Wilson
mesh_terms: D001185:Artificial Intelligence; D002965:Classification / Q000379:methods; D018360:Crystallography, X-Ray; D016002:Discriminant Analysis