Murphy Robert F
Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Methods Cell Biol. 2012;110:179-93. doi: 10.1016/B978-0-12-388403-9.00007-2.
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.
本章描述了从图像中学习亚细胞组织模型的方法。这些模型的主要用途预计是将其纳入细胞行为的复杂模拟中。目前大多数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一个单一的、理想化的隔室。构建蛋白质组中所有蛋白质的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。另一个可能同样重要的用途预计是用于测试和比较分析细胞图像的软件。基于细胞图像的筛选和检测（各种称为高内涵筛选、高内涵分析或高通量显微镜）中使用的算法的复杂性和精密性在不断增加，生成模型可用于生成图像以测试这些算法，其中预期答案是已知的。
