Classification of crystallization outcomes using deep convolutional neural networks
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| Authors | Andrew E. Bruno, Shawn Williams, Edward H. Snell, Patrick Charbonneau, David R. So, Julie Wilson, Janet Newman, Christopher J. Watkins, Vincent Vanhoucke |
| Journal/Conference Name | PLoS ONE |
| Paper Category | Artificial Intelligence |
| Paper 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. |
| Date of publication | 2018 |
| Code Programming Language | Multiple |
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