Establishing a training set through the visual analysis of crystallization trials. Part II: crystal examples
In the automated image analysis of crystallization experiments, representative examples of outcomes can be obtained rapidly. However, while the outcomes appear to be diverse, the number of crystalline outcomes can be small. To complement a training set from the visual observation of 147 456 crystall...
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| Veröffentlicht in: | Acta crystallographica. Section D, Biological crystallography. Biological crystallography., 2008-11, Vol.64 (11), p.1131-1137 |
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| container_title | Acta crystallographica. Section D, Biological crystallography. |
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| creator | Snell, Edward H. Lauricella, Angela M. Potter, Stephen A. Luft, Joseph R. Gulde, Stacey M. Collins, Robert J. Franks, Geoff Malkowski, Michael G. Cumbaa, Christian Jurisica, Igor DeTitta, George T. |
| description | In the automated image analysis of crystallization experiments, representative examples of outcomes can be obtained rapidly. However, while the outcomes appear to be diverse, the number of crystalline outcomes can be small. To complement a training set from the visual observation of 147 456 crystallization outcomes, a set of crystal images was produced from 106 and 163 macromolecules under study for the North East Structural Genomics Consortium (NESG) and Structural Genomics of Pathogenic Protozoa (SGPP) groups, respectively. These crystal images have been combined with the initial training set. A description of the crystal‐enriched data set and a preliminary analysis of outcomes from the data are described. |
| doi_str_mv | 10.1107/S0907444908028059 |
| format | Article |
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Part II: crystal examples</title><title>Acta crystallographica. Section D, Biological crystallography.</title><addtitle>Acta Cryst. D</addtitle><description>In the automated image analysis of crystallization experiments, representative examples of outcomes can be obtained rapidly. However, while the outcomes appear to be diverse, the number of crystalline outcomes can be small. To complement a training set from the visual observation of 147 456 crystallization outcomes, a set of crystal images was produced from 106 and 163 macromolecules under study for the North East Structural Genomics Consortium (NESG) and Structural Genomics of Pathogenic Protozoa (SGPP) groups, respectively. These crystal images have been combined with the initial training set. A description of the crystal‐enriched data set and a preliminary analysis of outcomes from the data are described.</description><subject>Computer Graphics</subject><subject>crystal images</subject><subject>Crystallization</subject><subject>Crystallography, X-Ray - classification</subject><subject>Crystallography, X-Ray - methods</subject><subject>Database Management Systems</subject><subject>Humans</subject><subject>image analysis</subject><subject>Image Processing, Computer-Assisted - classification</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Macromolecular Substances - chemistry</subject><subject>Models, Molecular</subject><subject>Polyethylene Glycols - chemistry</subject><subject>Polyethylene Glycols - metabolism</subject><subject>Research Papers</subject><subject>Teaching - methods</subject><subject>Teaching - trends</subject><issn>1399-0047</issn><issn>0907-4449</issn><issn>1399-0047</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1vEzEQhi0EoqXwA7ggn7htGdtre80BqZTSBoUPqYWIk-X1ehODsxvs3dLw63GUUIo45DQj-XlezXgQekrgmBCQLy5BgSzLUkEFtAKu7qFDwpQqAEp5_05_gB6l9A0AKGXyITogCigwTg5ROEuDqYNPC9_NscFDNL7btMkNeFjEfpwvcnX42qfRBGw6E9bJJ9y32MZ1lkPwv8zg-y673oR0jD-ZOODJ5OUfALsbs1wFlx6jB20m3JNdPUKf355dnV4U04_nk9OTaWF5Kaoij81Jw3kNDXMVtJRCrZitTStUS1lJmobWsm2IVFY6W3FnqahbS6g0UCrDjtCrbe5qrJeusa7LawW9in5p4lr3xut_Xzq_0PP-WlPBCCFVDni-C4j9j9GlQS99si4E07l-TFqoCkoq-F6QCSKASrUXpJmSUkAGyRa0sU8puvZ2bAJ6c3X939Wz8-zuvn-N3ZkzUG2Bnz649f5EffL1zesZp3zzFcVW9WlwN7eqid-1kExyPftwrt9fzt5dXUy_aMl-A3mfyWc</recordid><startdate>200811</startdate><enddate>200811</enddate><creator>Snell, Edward H.</creator><creator>Lauricella, Angela M.</creator><creator>Potter, Stephen A.</creator><creator>Luft, Joseph R.</creator><creator>Gulde, Stacey M.</creator><creator>Collins, Robert J.</creator><creator>Franks, Geoff</creator><creator>Malkowski, Michael G.</creator><creator>Cumbaa, Christian</creator><creator>Jurisica, Igor</creator><creator>DeTitta, George T.</creator><general>International Union of Crystallography</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>M7N</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>L7M</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>200811</creationdate><title>Establishing a training set through the visual analysis of crystallization trials. 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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection |
| subjects | Computer Graphics crystal images Crystallization Crystallography, X-Ray - classification Crystallography, X-Ray - methods Database Management Systems Humans image analysis Image Processing, Computer-Assisted - classification Image Processing, Computer-Assisted - methods Macromolecular Substances - chemistry Models, Molecular Polyethylene Glycols - chemistry Polyethylene Glycols - metabolism Research Papers Teaching - methods Teaching - trends |
| title | Establishing a training set through the visual analysis of crystallization trials. Part II: crystal examples |
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