Wilson, Julie Carol orcid.org/0000-0002-5171-8480, Bruno, A, Charbonneau, P et al. (6 more authors) (2018) Classification of crystallization outcomes using deep convolutional neural networks. PLOS one. e0198883. ISSN 1932-6203
Abstract
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.
Metadata
Authors/Creators: |
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Keywords: | Algorithms, Crystallization, Crystallography, X-Ray, Datasets as Topic, Image Processing, Computer-Assisted, Neural Networks (Computer) |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Mathematics (York) |
Depositing User: | Pure (York) |
Date Deposited: | 30 May 2018 11:50 |
Last Modified: | 06 Dec 2023 12:30 |
Published Version: | https://doi.org/10.1371/journal.pone.0198883 |
Status: | Published |
Refereed: | Yes |
Identification Number: | https://doi.org/10.1371/journal.pone.0198883 |
Related URLs: |
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Description: crystallisation classification
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