Automated Analysis of Human Protein Atlas Immunofluorescence Images.
Automated Analysis of Human Protein Atlas Immunofluorescence Images.
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DOI：
10.1109/isbi.2009.5193229
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Murphy RF
中科院分区：
文献类型：
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作者：
Newberg JY;Li J;Rao A;Pontén F;Uhlén M;Lundberg E;Murphy RF
人类蛋白质图谱是位置蛋白质组学数据的丰富来源。在这项工作中，我们提出了一种自动化的方法来处理和分类的阿特拉斯图像中的主要亚细胞模式。我们证明了两种不同的分类框架（支持向量机和随机森林）在确定亚细胞位置方面是有效的;我们可以分析超过3500张Atlas图像，准确度很高，所有样本的准确度高达87.5%，仅考虑我们最有信心的分类分配样本时，准确度高达98.5%。此外，在这两个框架中获得的功能被观察到是高度一致的和可推广的。此外，我们观察到，蛋白质与细胞标记物相关的特征在自动学习方法中尤其重要。
The Human Protein Atlas is a rich source of location proteomics data. In this work, we present an automated approach for processing and classifying major subcellular patterns in the Atlas images. We demonstrate that two different classification frameworks (support vector machine and random forest) are effective at determining subcellular locations; we can analyze over 3500 Atlas images with a high degree of accuracy, up to 87.5% for all of the samples and 98.5% when only considering samples in whose classification assignments we are most confident. Moreover, the features obtained in both of these frameworks are observed to be highly consistent and generalizable. Additionally, we observe that the features relating the proteins to cell markers are especially important in automated learning approaches.