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Classification of crystallization outcomes using deep convolutional neural networks.

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.

SUBMITTER: Bruno AE

PROVIDER: S-EPMC6010233 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Classification of crystallization outcomes using deep convolutional neural networks.

Bruno Andrew E AE Charbonneau Patrick P Newman Janet J Snell Edward H EH So David R DR Vanhoucke Vincent V Watkins Christopher J CJ Williams Shawn S Wilson Julie J

PloS one 20180620 6

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 s ...[more]

PMID: 29924841

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