| Autor: |
Andrew E Bruno, Patrick Charbonneau, Janet Newman, Edward H Snell, David R So, Vincent Vanhoucke, Christopher J Watkins, Shawn Williams, Julie Wilson |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
PLoS ONE, Vol 13, Iss 6, p e0198883 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0198883 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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