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 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.





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Publication Info

Bruno, Andrew E, Patrick Charbonneau, Janet Newman, Edward H Snell, David R So, Vincent Vanhoucke, Christopher J Watkins, Shawn Williams, et al. (2018). Classification of crystallization outcomes using deep convolutional neural networks. PloS one, 13(6). p. e0198883. 10.1371/journal.pone.0198883 Retrieved from

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