Center for Computational Research, University at Buffalo, Buffalo, New York, United States of America.
Department of Chemistry, Duke University, Durham, North Carolina, United States of America.
PLoS One. 2018 Jun 20;13(6):e0198883. doi: 10.1371/journal.pone.0198883. eCollection 2018.
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.
结晶结果的机器识别（MARCO）计划从各种来源和设置中收集了大约五十万张大分子结晶实验的注释图像。在这里，最先进的机器学习算法在这个数据集的不同部分进行训练和测试。我们发现，超过 94%的测试图像可以被正确标记，而不管它们的实验来源如何。由于晶体识别是高密度筛选和结晶实验系统分析的关键，因此这种方法为工业和基础研究应用开辟了道路。
