Classification of crystallization outcomes using deep convolutional neural networks.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Bruno, AE; Charbonneau, P; Newman, J; Snell, EH; So, DR; Vanhoucke, V; Watkins, CJ; Williams, S; Wilson, J

Published Date

  • January 2018

Published In

Volume / Issue

  • 13 / 6

Start / End Page

  • e0198883 -

PubMed ID

  • 29924841

Pubmed Central ID

  • 29924841

Electronic International Standard Serial Number (EISSN)

  • 1932-6203

International Standard Serial Number (ISSN)

  • 1932-6203

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0198883

Language

  • eng