title: Establishing a training set through the visual analysis of crystallization trials. Part I: approximately 150,000 images.
abstract: Structural crystallography aims to provide a three-dimensional representation of macromolecules. Many parts of the multistep process to produce the three-dimensional structural model have been automated, especially through various structural genomics projects. A key step is the production of crystals for diffraction. The target macromolecule is combined with a large and chemically diverse set of cocktails with some leading ideally, but infrequently, to crystallization. A variety of outcomes will be observed during these screening experiments that typically require human interpretation for classification. Human interpretation is neither scalable nor objective, highlighting the need to develop an automatic computer-based image classification. As a first step towards automated image classification, 147,456 images representing crystallization experiments from 96 different macromolecular samples were manually classified. Each image was classified by three experts into seven predefined categories or their combinations. The resulting data where all three observers are in agreement provides one component of a truth set for the development and rigorous testing of automated image-classification systems and provides information about the chemical cocktails used for crystallization. In this paper, the details of this study are presented.
authors.affiliation: Hauptman-Woodward Medical Research Institute, 700 Ellicott Street, Buffalo, NY 14203, USA. esnell@hwi.buffalo.edu
authors.forename: Edward H
authors.initials: EH
authors.lastname: Snell
authors.forename: Joseph R
authors.initials: JR
authors.lastname: Luft
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Potter
authors.forename: Angela M
authors.initials: AM
authors.lastname: Lauricella
authors.forename: Stacey M
authors.initials: SM
authors.lastname: Gulde
authors.forename: Michael G
authors.initials: MG
authors.lastname: Malkowski
authors.forename: Mary
authors.initials: M
authors.lastname: Koszelak-Rosenblum
authors.forename: Meriem I
authors.initials: MI
authors.lastname: Said
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Smith
authors.forename: Christina K
authors.initials: CK
authors.lastname: Veatch
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Collins
authors.forename: Geoff
authors.initials: G
authors.lastname: Franks
authors.forename: Max
authors.initials: M
authors.lastname: Thayer
authors.forename: Christian
authors.initials: C
authors.lastname: Cumbaa
authors.forename: Igor
authors.initials: I
authors.lastname: Jurisica
authors.forename: George T
authors.initials: GT
authors.lastname: Detitta
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D003460:Crystallization; D018360:Crystallography, X-Ray / Q000145:classification / Q000379:methods*; D001330:Electronic Data Processing; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000145:classification / Q000379:methods*; D046911:Macromolecular Substances / Q000737:chemistry*; D008958:Models, Molecular; D013663:Teaching / Q000379:methods* / Q000639:trends
chemical_list: D046911:Macromolecular Substances