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 sampling and the systematic
analysis of crystallization experiments, this approach opens the door to both industrial
and fundamental research applications.
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 sampling and the systematic
analysis of crystallization experiments, this approach opens the door to both industrial
and fundamental research applications.
Research Areas
