Objective clustering of proteins based on subcellular location patterns.
Objective clustering of proteins based on subcellular location patterns.
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DOI：
10.1155/jbb.2005.87
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发表时间：
2005-06-30
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：
Chen X;Murphy RF
蛋白质组学的目标是对所有蛋白质进行完整的表征。表征亚细胞位置的努力仅限于将蛋白质分配到细胞器的一般类别。我们以前设计的数值特征来描述显微镜图像中的位置模式，并开发了自动分类器，以高精度区分主要的亚细胞模式（包括视觉检查无法区分的模式）。结果表明，自动确定哪些蛋白质在给定细胞类型中共享单一位置模式是可行的。我们描述了一种自动化的方法，选择最好的功能集来描述图像的蛋白质的给定集合，并构建一个有效的分区的蛋白质的位置。一个有限的蛋白质集的例子。随着更多的数据变得可用，这种方法可以产生第一次为蛋白质定位的客观系统学，并提供了一个重要的起点，发现确定定位的序列基序。
The goal of proteomics is the complete characterization of all proteins. Efforts to characterize subcellular location have been limited to assigning proteins to general categories of organelles. We have previously designed numerical features to describe location patterns in microscope images and developed automated classifiers that distinguish major subcellular patterns with high accuracy (including patterns not distinguishable by visual examination). The results suggest the feasibility of automatically determining which proteins share a single location pattern in a given cell type. We describe an automated method that selects the best feature set to describe images for a given collection of proteins and constructs an effective partitioning of the proteins by location. An example for a limited protein set is presented. As additional data become available, this approach can produce for the first time an objective systematics for protein location and provide an important starting point for discovering sequence motifs that determine localization.