文章针对蛋白质二级结构预测这一复杂非线性模式分类问题，提出了基于径向基函数的预测方法。
Aiming at solving the complicated non-linear pattern classification problem of protein secondary structure prediction, a new method based on radial basis function is proposed.
分析了多分类器融合算法的理论框架，并采用决策模板算法对蛋白质结构类的预测问题进行了研究。
We investigate the theoretical framework of multiple classifiers fusion, and apply the decision template algorithms to classify the protein secondary structural classes.
提出了根据蛋白质二级结构主序列对蛋白质结构型进行识别(分类)的方法。
Based on this, two methods for the recognition of the structural class of a protein are proposed.
蛋白质酶谱和结构的研究，对于深入了解生物的进化和分类关系具有重要的意义。
Studying the structure and zymogram of proteid is important to realize the organism evolution and classification.
结果：我们提出了一个半自动的程序用于获得蛋白质域结构的一个新的层次分类(CATH)。
RESULTS: We present a semi-automatic procedure for deriving a novel hierarchical classification of protein domain structures (CATH).
我们的分类中的四个主要层次是蛋白质类(C)，结构(A)，拓扑结构(T)和同源超家族(H)。
The four main levels of our classification are protein class (c), architecture (a), topology (t) and homologous superfamily (h).
实验结果与对比表明，该方法不仅具有低维的特征，而且有效地实现了多类蛋白质结构分类识别。
The experiments and the comparison results demonstrate that the presented method can not only product low-dimensional feature and also achieve effective classification of PSS.
蛋白质结构分类分为多个层次，如何对蛋白质结构进行定量分类和自动分析是目前研究的重点。
Protein structure classification is based on several levels. In recent years, the topic is to find methods to classify and analyze automatically protein structure.
当前，至少80%的主要蛋白质序列数据库能够使用这些工具进行分类，因此提供了大量数据开始分析蛋白质域结构。
Currently at least 80% of the main protein sequence databases can be classified using these tools, thus providing a large data set to work from for analyzing protein domain architectures.
本文选择蛋白质二级结构数据为主要的研究对象，应用数据挖掘技术和机器学习中的动态规划理论进行蛋白质结构分类。
Protein second structure data is chosen as main study object, and data mining and dynamic programming are applied to protein structure classification.
数据库的分类分别为蛋白质组注释库、蛋白质环分类、 蛋白质 结构域预测和水稻矮小和抗病毒库。
The sorts of the databases are respectively proteome annotation base, protein loop classification, protein domain prediction and rice dwarf phytoreovirus bank.
数据库的分类分别为蛋白质组注释库、蛋白质环分类、 蛋白质 结构域预测和水稻矮小和抗病毒库。
The sorts of the databases are respectively proteome annotation base, protein loop classification, protein domain prediction and rice dwarf phytoreovirus bank.
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