CellOrganizer: Image-derived models of subcellular organization and protein distribution.
CellOrganizer: Image-derived models of subcellular organization and protein distribution.
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DOI：
10.1016/b978-0-12-388403-9.00007-2
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发表时间：
2012
影响因子：
--
通讯作者：
Murphy, Robert F.
中科院分区：
文献类型：
--
作者：
Murphy, Robert F.
本章描述了从图像中学习亚细胞组织模型的方法。这些模型的主要效用预计将被纳入复杂的细胞行为模拟。大多数当前的细胞模拟根本不考虑蛋白质的空间组织，或者将每种细胞器类型视为单一的理想化隔室。为蛋白质组中的所有蛋白质构建生成模型并将其用于空间精确模拟的能力有望提高细胞行为模型的准确性。第二个用途，潜在的同等重要性，预计将在测试和比较软件分析细胞图像。在基于细胞图像的筛选和测定（不同地称为高内容筛选、高内容分析或高通量显微术）中使用的算法的复杂性和复杂性不断增加，并且生成模型可以用于产生用于测试这些算法的图像，其中预期的答案是已知的。
This chapter describes approaches for learning models of subcellular organization from images. The primary utility of these models is expected to be from incorporation into complex simulations of cell behaviors. Most current cell simulations do not consider spatial organization of proteins at all, or treat each organelle type as a single, idealized compartment. The ability to build generative models for all proteins in a proteome and use them for spatially accurate simulations is expected to improve the accuracy of models of cell behaviors. A second use, of potentially equal importance, is expected to be in testing and comparing software for analyzing cell images. The complexity and sophistication of algorithms used in cell image-based screens and assays (variously referred to as high content screening, high content analysis, or high throughput microscopy) is continuously increasing, and generative models can be used to produce images for testing these algorithms in which the expected answer is known.