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首页> 外文期刊>Acta crystallographica.Section D. Biological crystallography >Image-based crystal detection: a machine-learning approach.
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Image-based crystal detection: a machine-learning approach.

机译：基于图像的晶体检测:机器学习的方法。

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摘要

The ability of computers to learn from and annotate large databases of crystallization-trial images provides not only the ability to reduce the workload of crystallization studies, but also an opportunity to annotate crystallization trials as part of a framework for improving screening methods. Here, a system is presented that scores sets of images based on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing crystallization-analysis pipelines, whereby specialists examine images as they normally would with the exception that the images appear in rank order according to a simple real-valued score. Promising results are shown for 319 112 images associated with 150 structures solved by the Joint Center for Structural Genomics pipeline during the 2006-2007 year. Overall, the algorithm achieves a mean receiver operating characteristic score of 0.919 and a 78% reduction in human effort per set when considering an absolutescore cutoff for screening images, while incurring a loss of five out of 150 structures.
机译：电脑学习和的能力注释crystallization-trial的大型数据库图像提供了不仅能够减少结晶研究的工作量,但也注释结晶试验的机会作为框架的一部分,提高筛查方法。的图像基于的可能性被一个包含晶体材料机器学习算法。纳入现有的crystallization-analysis管道,他们通常会专家检查图像除了这些图片出现在排名根据一个简单的实值评分。有前景的结果为319 112张图片所示与150结构解决联合中心结构基因组学管道在2006 - 2007年。达到一个意味着接收器的操作特点得分0.919和人类减少了78%每集在考虑一个absolutescore努力截止扫描图像,而引起五的150结构的损失。

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