Object type recognition for automated analysis of protein subcellular location
Object type recognition for automated analysis of protein subcellular location
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DOI：
10.1109/tip.2005.852456
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发表时间：
2005-09-01
影响因子：
10.6
通讯作者：
Murphy, RF
中科院分区：
文献类型：
--
作者：
Zhao, T;Velliste, M;Murphy, RF
位置蛋白质组学的新领域旨在提供给定细胞类型中表达的所有蛋白质的亚细胞位置的全面、客观的表征。先前的工作表明，自动分类器可以高精度识别荧光显微镜图像中所有主要亚细胞细胞器和结构的模式。然而，由于某些蛋白质可能存在于多个细胞器中，因此本文解决了一项更困难的任务：识别由两种或多种基本模式混合而成的模式。该方法利用基于对象的图像模型，其中位置模式的每个图像都由一组不同的、学习类型的对象表示。使用学习对象类型，然后根据对象类型计算单元级特征的两阶段方法，可以很好地识别基本位置模式。给定对象类型，构建多项混合模型来识别混合模式。在适当的条件下，合成的混合物模式可以以超过80%的准确度分解，这首次表明通过计算将亚细胞模式分解为基本细胞器模式的问题是可以解决的。
The new field of location proteomics seeks to provide a comprehensive, objective characterization of the subcellular locations of all proteins expressed in a given cell type. Previous work has demonstrated that automated classifiers can recognize the patterns of all major subcellular organelles and structures in fluorescence microscope images with high accuracy. However, since some proteins may be present in more than one organelle, this paper addresses a more difficult task: recognizing a pattern that is a mixture of two or more fundamental patterns. The approach utilizes an object-based image model, in which each image of a location pattern is represented by a set of objects of distinct, learned types. Using a two-stage approach in which object types are learned and then cell-level features are calculated based on the object types, the basic location patterns were well recognized. Given the object types, a multinomial mixture model was built to recognize mixture patterns. Under appropriate conditions, synthetic mixture patterns can be decomposed with over 80% accuracy, which, for the first time, shows that the problem of computationally decomposing subcellular patterns into fundamental organelle patterns can be solved.