title: Image-based crystal detection: a machine-learning approach.
abstract: The ability of computers to learn from and annotate large databases of crystallization-trial images provides not only the ability to reduce the workload of crystallization studies, but also an opportunity to annotate crystallization trials as part of a framework for improving screening methods. Here, a system is presented that scores sets of images based on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing crystallization-analysis pipelines, whereby specialists examine images as they normally would with the exception that the images appear in rank order according to a simple real-valued score. Promising results are shown for 319 112 images associated with 150 structures solved by the Joint Center for Structural Genomics pipeline during the 2006-2007 year. Overall, the algorithm achieves a mean receiver operating characteristic score of 0.919 and a 78% reduction in human effort per set when considering an absolute score cutoff for screening images, while incurring a loss of five out of 150 structures.
authors.affiliation: University of California at San Diego, USA.
authors.forename: Roy
authors.initials: R
authors.lastname: Liu
authors.forename: Yoav
authors.initials: Y
authors.lastname: Freund
authors.forename: Glen
authors.initials: G
authors.lastname: Spraggon
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003460:Crystallization; D018360:Crystallography, X-Ray / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D003628:Database Management Systems / Q000191:economics / Q000295:instrumentation; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve
chemical_list: D011506:Proteins