Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1371/journal.pone.0198883 | PLOS ONE |
Field | DocType | Volume |
Protein crystallization,Training set,Pattern recognition,Computer science,Convolutional neural network,Crystallization,Datasets as Topic,Artificial intelligence,Artificial neural network,Contextual image classification,Machine recognition | Journal | 13 |
Issue | ISSN | Citations |
6 | 1932-6203 | 2 |
PageRank | References | Authors |
0.54 | 12 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew E. Bruno | 1 | 29 | 4.74 |
| Patrick Charbonneau | 2 | 2 | 0.54 |
| Janet Newman | 3 | 2 | 1.22 |
| Edward H. Snell | 4 | 2 | 0.54 |
| David R. So | 5 | 2 | 2.23 |
| Vincent Vanhoucke | 6 | 4735 | 213.63 |
| Shawn Williams | 7 | 2 | 0.54 |
| Julie Wilson | 8 | 4 | 1.62 |