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基于支持向量机融合网络的蛋白质折叠子识别研究
引用本文:施建宇,潘 泉,张绍武,梁 彦.基于支持向量机融合网络的蛋白质折叠子识别研究[J].生物化学与生物物理进展,2006,33(2):155-162.
作者姓名:施建宇  潘 泉  张绍武  梁 彦
作者单位:1. 西北工业大学自动化学院,西安,710072
2. 西北工业大学自动化学院,西安,710072;西北工业大学生命科学院,西安,710072
摘    要:在不依赖于序列相似性的条件下,蛋白质折叠子识别是一种分析蛋白质结构的重要方法.提出了一种三层支持向量机融合网络,从蛋白质的氨基酸序列出发,对27类折叠子进行识别.融合网络使用支持向量机作为成员分类器,采用“多对多”的多类分类策略,将折叠子的6种特征分为主要特征和次要特征,构建了多个差异的融合方案,然后对这些融合方案进行动态选择得到最终决策.当分类之前难以确定哪些参与组合的特征种类能够使分类结果最好时,提供了一种可靠的解决方案来自动选择特征信息互补最大的组合,保证了最佳分类结果.最后,识别系统对独立测试样本的总分类精度达到61.04%.结果和对比表明,此方法是一种有效的折叠子识别方法.

关 键 词:折叠子识别  支持向量机  分类器融合  动态选择
收稿时间:2005-08-23
修稿时间:2005-08-232005-09-30

Protein Fold Recognition With Support Vector Machines Fusion Network
SHI Jian-Yu,PAN Quan,ZHANG Shao-Wu and LIANG Yan.Protein Fold Recognition With Support Vector Machines Fusion Network[J].Progress In Biochemistry and Biophysics,2006,33(2):155-162.
Authors:SHI Jian-Yu  PAN Quan  ZHANG Shao-Wu and LIANG Yan
Abstract:One of the important approaches to structure analysis is protein fold recognition, which is oftenapplied when there is no significant sequence similarity between structurally similar proteins. A framework with athree-layer support vector machines fusion network (SFN) is presented. The framework is applied to 27-classprotein fold recognition from primary structure of proteins. SFN uses support vector machines as memberclassifiers, and adopts All-Versus-All as multi-class categorization. Six groups of features are divided into majorand minor ones by SFN, and several diversity fusion schemes are correspondingly built. The final decision is madeby dynamic selection of the results of all fusion schemes. When it is still difficult to know what kind of fusion offeature groups can achieve good prediction,SFN is a dependable solution by selecting the optimal fusion offeature groups automatically, which can ensure the best recognition. Overall recognition system achieves 61.04%fold prediction accuracy on the independent test dataset. The results and the comparison with other approachesdemonstrate the effectiveness of SFN, and thus encourage its further exploration.
Keywords:protein fold recognition  support vector machines (SVM)  classifier fusion  dynamic selection
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