Classification of crystallization outcomes using deep convolutional neural networks
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of...
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| Veröffentlicht in: | PloS one 2018-06, Vol.13 (6), p.e0198883-e0198883 |
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| creator | Bruno, Andrew E Charbonneau, Patrick Newman, Janet Snell, Edward H So, David R Vanhoucke, Vincent Watkins, Christopher J Williams, Shawn Wilson, Julie |
| description | The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications. |
| doi_str_mv | 10.1371/journal.pone.0198883 |
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Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0198883</identifier><identifier>PMID: 29924841</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Artificial intelligence ; Artificial neural networks ; Automatic classification ; Biological research ; Biology and Life Sciences ; Computer and Information Sciences ; Crystallization ; Crystallography ; Crystallography, X-Ray ; Datasets as Topic ; Experiments ; Identification and classification ; Image Processing, Computer-Assisted ; Information processing ; International conferences ; Learning algorithms ; Machine learning ; Macromolecules ; Methods ; Molecular biology ; Neural networks ; Neural Networks (Computer) ; Physical Sciences ; Proteins ; Recognition ; Research and Analysis Methods ; Scientific classification ; Social Sciences</subject><ispartof>PloS one, 2018-06, Vol.13 (6), p.e0198883-e0198883</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Bruno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 Bruno et al 2018 Bruno et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-630ad2318126ebc0ef7a01135d74f41e56e6f67794d8ebb570d724b4caef4d5e3</citedby><cites>FETCH-LOGICAL-c758t-630ad2318126ebc0ef7a01135d74f41e56e6f67794d8ebb570d724b4caef4d5e3</cites><orcidid>0000-0001-7174-0821 ; 0000-0003-0544-2791 ; 0000-0002-7317-9959 ; 0000-0002-5171-8480 ; 0000-0003-2666-3219</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010233/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010233/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29924841$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bruno, Andrew E</creatorcontrib><creatorcontrib>Charbonneau, Patrick</creatorcontrib><creatorcontrib>Newman, Janet</creatorcontrib><creatorcontrib>Snell, Edward H</creatorcontrib><creatorcontrib>So, David R</creatorcontrib><creatorcontrib>Vanhoucke, Vincent</creatorcontrib><creatorcontrib>Watkins, Christopher J</creatorcontrib><creatorcontrib>Williams, Shawn</creatorcontrib><creatorcontrib>Wilson, Julie</creatorcontrib><title>Classification of crystallization outcomes using deep convolutional neural networks</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Automatic classification</subject><subject>Biological research</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Crystallization</subject><subject>Crystallography</subject><subject>Crystallography, X-Ray</subject><subject>Datasets as Topic</subject><subject>Experiments</subject><subject>Identification and classification</subject><subject>Image Processing, Computer-Assisted</subject><subject>Information processing</subject><subject>International conferences</subject><subject>Learning algorithms</subject><subject>Machine 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| subjects | Algorithms Artificial intelligence Artificial neural networks Automatic classification Biological research Biology and Life Sciences Computer and Information Sciences Crystallization Crystallography Crystallography, X-Ray Datasets as Topic Experiments Identification and classification Image Processing, Computer-Assisted Information processing International conferences Learning algorithms Machine learning Macromolecules Methods Molecular biology Neural networks Neural Networks (Computer) Physical Sciences Proteins Recognition Research and Analysis Methods Scientific classification Social Sciences |
| title | Classification of crystallization outcomes using deep convolutional neural networks |
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