Marabini R, Carazo J M
Centro Nacional de Biotecnología (CSIC), Universidad Autónoma, Madrid, Spain.
Biophys J. 1994 Jun;66(6):1804-14. doi: 10.1016/S0006-3495(94)80974-9.
The goal of this work was to analyze an image data set and to detect the structural variability within this set. Two algorithms for pattern recognition based on neural networks are presented, one that performs an unsupervised classification (the self-organizing map) and the other a supervised classification (the learning vector quantization). The approach has a direct impact in current strategies for structural determination from electron microscopic images of biological macromolecules. In this work we performed a classification of both aligned but heterogeneous image data sets as well as basically homogeneous but otherwise rotationally misaligned image populations, in the latter case completely avoiding the typical reference dependency of correlation-based alignment methods. A number of examples on chaperonins are presented. The approach is computationally fast and robust with respect to noise. Programs are available through ftp.
这项工作的目标是分析一个图像数据集，并检测该数据集中的结构变异性。提出了两种基于神经网络的模式识别算法，一种执行无监督分类（自组织映射），另一种执行监督分类（学习矢量量化）。该方法对当前从生物大分子的电子显微镜图像确定结构的策略有直接影响。在这项工作中，我们对既对齐但异质的图像数据集以及基本同质但在旋转上未对齐的图像群体进行了分类，在后一种情况下完全避免了基于相关性的对齐方法典型的参考依赖性。给出了一些关于伴侣蛋白的例子。该方法计算速度快且对噪声具有鲁棒性。程序可通过ftp获取。
