Topology representing network enables highly accurate classification of protein images taken by cryo electron-microscope without masking
Topology representing network enables highly accurate classification of protein images taken by cryo electron-microscope without masking
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DOI：
10.1016/j.jsb.2003.08.005
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发表时间：
2003-09-01
影响因子：
3
通讯作者：
Sato, C
中科院分区：
文献类型：
--
作者：
Ogura, T;Iwasaki, K;Sato, C
在单粒子分析中，利用电子显微镜(EM)构建蛋白质的三维结构。由于这些图像通常噪声很大，这种三维重建的主要过程是根据图像的欧拉角对图像进行分类，然后对每个分类组中的图像进行平均以降低噪声水平。在我们新开发的分类策略中，我们引入了一种拓扑表示网络(TRN)方法。它是生长神经气体网络(GNG)的一种改进方法。在该系统中，响应于通过生长过程输入的图像，自动确定网络结构。在没有掩蔽过程的情况下学习之后，GNG创建输入的清晰平均值作为多维空间中的单位坐标，然后用于分类。在这个过程中，高度相关的单元之间自动建立连接，并且它们的位置被移动，其中输入分布在多维空间中。因此，形成了几组相互独立的连接单元。虽然这个空间中单元之间的相互关系不容易理解，但我们成功地解决了这个问题，将单元位置转换到二维空间，并用模拟退火法进一步优化了单元位置。在优化的二维图中，单元间联系的可视化提供了丰富的聚类信息。该方法作为一种分类方法，明显优于多元统计分析(MSA)和自组织映射(SOM)分类方法，并提供了一种可靠的分类方法，可以在不掩蔽噪声的情况下使用。(C)2003 Elsevier Inc.保留所有权利。
In single-particle analysis, a three-dimensional (3-D) structure of a protein is constructed using electron microscopy (EM). As these images are very noisy in general, the primary process of this 3-D reconstruction is the classification of images according to their Euler angles, the images in each classified group then being averaged to reduce the noise level. In our newly developed strategy of classification, we introduce a topology representing network (TRN) method. It is a modified method of a growing neural gas network (GNG). In this system, a network structure is automatically determined in response to the images input through a growing process. After learning without a masking procedure, the GNG creates clear averages of the inputs as unit coordinates in multidimensional space, which are then utilized for classification. In the process, connections are automatically created between highly related units and their positions are shifted where the inputs are distributed in multi-dimensional space. Consequently, several separated groups of connected units are formed. Although the interrelationship of units in this space are not easily understood, we succeeded in solving this problem by converting the unit positions into two-dimensional (2-D) space, and by further optimizing the unit positions with the simulated annealing (SA) method. In the optimized 2-D map, visualization of the connections of units provided rich information about clustering. As demonstrated here, this method is clearly superior to both the multi-variate statistical analysis (MSA) and the self-organizing map (SOM) as a classification method and provides a first reliable classification method which can be used without masking for very noisy images. (C) 2003 Elsevier Inc. All rights reserved.