A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells
A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells
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DOI：
10.1093/bioinformatics/17.12.1213
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发表时间：
2001-12-01
期刊：
影响因子：
5.8
通讯作者：
Murphy, RF
中科院分区：
文献类型：
--
作者：
Boland, MV;Murphy, RF
动机：蛋白质亚细胞定位的评估对于蛋白质组学研究至关重要，因为定位信息为蛋白质的序列、结构和功能提供了背景。下文所述的工作首次以定量、全面的方式探讨蛋白质的亚细胞定位。
结果：利用荧光显微镜收集了十种不同亚细胞模式（包括所有主要细胞器）的图像。使用多种数值特征对这些模式进行了描述，包括泽尼克矩、哈拉里克纹理特征以及一组专门为此目的开发的新特征。为了测试这些特征的有效性，利用它们训练了一个神经网络分类器。该分类器能够正确识别平均83%的呈现十种模式之一的先前未见过的细胞。然后使用相同的分类器识别均匀制备的先前未见过的细胞组，准确率达到98%。
Motivation: Assessment of protein subcellular location is crucial to proteomics efforts since localization information provides a context for a protein's sequence, structure, and function. The work described below is the first to address the subcellular localization of proteins in a quantitative., comprehensive manner.Results: Images for ten different subcellular patterns (including all major organelles) were collected Using fluorescence microscopy. The patterns were described using a variety of numeric features, including Zernike moments, Haralick texture features, and a set, of new features developed specifically for this purpose. To test the usefulness of these features, they were used to train a neural network classifier. The classifier was able to correctly recognize an average of 83% of previously unseen cells showing one of the ten patterns. The same classifier was then used to recognize previously unseen sets of homogeneously prepared cells with 98% accuracy.