Mapping and fuzzy classification of macromolecular images using self-organizing neural networks
Mapping and fuzzy classification of macromolecular images using self-organizing neural networks
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DOI：
10.1016/s0304-3991(00)00022-x
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发表时间：
2000-07-01
期刊：
影响因子：
2.2
通讯作者：
Carazo, JM
中科院分区：
文献类型：
--
作者：
Pascual, A;B치rcena, M;Carazo, JM
在这项工作中，研究了模糊的Kohonen聚类网络（FKCN）在生物大分子的电子显微镜图像的无监督分类中的有效性。该算法结合了Kohonen的自组织特征图（SOFM）和模糊C均值（FCM），以获得具有从两者继承的最佳属性的强大聚类技术。使用SOFM的探索性数据分析也作为最终聚类之前的步骤提出。从枯草芽孢杆菌噬菌体SPP1中从G40p解旋酶获得的两个不同的数据集已用于测试所提出的方法，该方法由单个图像的2458个旋转功率谱和另一个由来自同一大分子的338张图像组成。将FKCN的结果与自组织特征图（SOFM）和手动分类进行了比较。实验结果证明，该新技术适合于使用大型，高维和嘈杂的数据集，因此，它被认为用作电子显微镜中的分类工具。 （c）2000 Elsevier Science B.V.保留所有权利。
In this work the effectiveness of the fuzzy kohonen clustering network (FKCN) in the unsupervised classification of electron microscopic images of biological macromolecules is studied. The algorithm combines Kohonen's self-organizing feature maps (SOFM) and Fuzzy c-means (FCM) in order to obtain a powerful clustering technique with the best properties inherited from both. Exploratory data analysis using SOFM is also presented as a step previous to final clustering. Two different data sets obtained from the G40P helicase from B. Subtilis bacteriophage SPP1 have been used for testing the proposed method, one composed of 2458 rotational power spectra of individual images and the other composed by 338 images from the same macromolecule. Results of FKCN are compared with self-organizing feature maps (SOFM) and manual classification. Experimental results prove that this new technique is suitable for working with large, high-dimensional and noisy data sets and, thus, it is proposed to be used as a classification tool in electron microscopy. (C) 2000 Elsevier Science B.V. All rights reserved.