Protein crystallization analysis on the World Community Grid.
Protein crystallization analysis on the World Community Grid.
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DOI：
10.1007/s10969-009-9076-9
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Jurisica, Igor
中科院分区：
文献类型：
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作者：
Cumbaa, Christian A;Jurisica, Igor
我们开发了一个图像分析和分类系统，用于自动评分高通量蛋白质结晶试验的图像。该系统的图像分析由世界社区网格上的帮助征服癌症（HCC）项目执行。HCC计算来自Hauptman-Woodward医学研究所高通量筛选实验室的微批油下图像的12,375个不同的图像特征。使用hcc计算图像特征和165,351张手工评分图像的大规模训练集，我们训练了多个随机森林分类器，这些分类器可以准确识别多种结晶结果，包括晶体、透明滴、沉淀等。该系统成功识别了80%的晶体图像、89%的沉淀图像和98%的透明水滴图像。本文的在线版本（doi:10.1007/s10969-009-9076-9）包含补充材料，可供授权用户使用。
We have developed an image-analysis and classification system for automatically scoring images from high-throughput protein crystallization trials. Image analysis for this system is performed by the Help Conquer Cancer (HCC) project on the World Community Grid. HCC calculates 12,375 distinct image features on microbatch-under-oil images from the Hauptman-Woodward Medical Research Institute’s High-Throughput Screening Laboratory. Using HCC-computed image features and a massive training set of 165,351 hand-scored images, we have trained multiple Random Forest classifiers that accurately recognize multiple crystallization outcomes, including crystals, clear drops, precipitate, and others. The system successfully recognizes 80% of crystal-bearing images, 89% of precipitate images, and 98% of clear drops. The online version of this article (doi:10.1007/s10969-009-9076-9) contains supplementary material, which is available to authorized users.