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arXiv · 1803.10342

Classification of crystallization outcomes using deep convolutional neural networks

Andrew E. Bruno·Patrick Charbonneau·Janet Newman·Edward H. Snell·David R. So·Vincent Vanhoucke·Christopher J. Watkins·Shawn Williams·Julie Wilson
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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.

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Andrew E. Bruno, Patrick Charbonneau, Janet Newman, Edward H. Snell, David R. So, Vincent Vanhoucke, Christopher J. Watkins, Shawn Williams, Julie Wilson. 2018-05-26. Classification of crystallization outcomes using deep convolutional neural networks. https://doi.org/10.1371/journal.pone.0198883

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