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.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1371/journal.pone.0198883

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 https://hdl.handle.net/10161/17392.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.