Automated interpretation of protein subcellular location patterns - Implications for early cancer detection and assessment
Automated interpretation of protein subcellular location patterns - Implications for early cancer detection and assessment
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DOI：
10.1196/annals.1310.013
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发表时间：
2004-01-01
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影响因子：
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通讯作者：
Murphy, RF
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文献类型：
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作者：
Murphy, RF
荧光显微镜是分析蛋白质亚细胞分布的有力工具，但这种能力尚未得到充分利用，因为这些分布的大多数分析都是通过目视检查完成的。使用广泛用于其它领域的自动模式识别方法可以克服该限制。本文总结的工作表明，自动化系统可以识别培养细胞的二维和三维图像中的主要细胞器的模式，这些系统可以区分类似的模式比视觉检查。这些系统的基础是亚细胞位置特征集，这些特征集捕捉亚细胞模式的本质，而对显微镜图像中细胞的大小、形状和方向发生的广泛变化不敏感。这些特征也可用于对两种条件之间的蛋白质分布进行敏感的统计比较，例如在存在和不存在药物的情况下。还讨论了可能使用的自动模式分析方法，以提高检测异常细胞的癌或癌前组织。
Fluorescence microscopy is a powerful tool for analyzing the subcellular distributions of proteins, but that power has not been fully utilized because most analysis of those distributions has been done by visual examination. this limitation can be overcome using automated pattern recognition methods widely used in other fields. This article summarizes work demonstrating that automated systems can recognize the patterns of major organelles in both two- and three-dimensional images of cultured cells, and that these systems can distinguish similar patterns better than visual examination. The basis for these systems are sets of Subcellular Location Features that capture the essence of subcellular patterns without being sensitive to the extensive variation that occurs in the size, shape, and orientation of cells in microscope images. These features can also be used to make sensitive, statistical comparisons of the distribution of a protein between two conditions, such as in the presence and absence of a drug. The possible use of automated pattern analysis methods for improving detection of abnormal cells in cancerous or precancerous tissues is also discussed.