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Using PLS Variable Selection Method to Build the Model between the Number of tria-coupled amino acid and the Number of Protein Secondary Structure

机译:使用PLS变量选择方法建立三联氨基酸数目与蛋白质二级结构数目之间的模型

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摘要

The relation between protein sequence and protein secondary structure is very important, which has been studied by the method of building the model. Based on the models (between pair-coupled amino acid and protein secondary structure) in literature, the models between the number of tria-coupled amino acid in protein sequence and the number of protein secondary structure have been built The models are more accurately reflect the relation between protein sequence and protein secondary structure. The models are more suitable to deal with the data in which the length of protein sequence varies a lot Comparing with the models between pair-coupled amino acid and protein secondary structure, the models contain more information about coupling effect among varies kinds of amino acids, and therefore are of the higher fitting accuracy. The data set in the study is very large, because the kinds of tria-coupled amino acid in protein sequence are very big (4200) and the number of samples from DSSP database is also very large (11600). The results indicate that the PLs variable selection method is effective to deal with the huge data modeling problem in which the number of variables is 4200 and the number of samples is 11600.
机译:蛋白质序列与蛋白质二级结构之间的关系非常重要,已通过建立模型的方法对其进行了研究。根据文献中的模型(配对氨基酸和蛋白质二级结构之间),建立了蛋白质序列中三联氨基酸数量与蛋白质二级结构数量之间的模型。这些模型可以更准确地反映蛋白质序列与蛋白质二级结构之间的关系。该模型更适合处理蛋白质序列长度变化很大的数据。与配对氨基酸和蛋白质二级结构之间的模型相比,该模型包含有关各种氨基酸之间偶联作用的更多信息,因此具有较高的拟合精度。该研究中的数据集非常大,因为蛋白质序列中三联氨基酸的种类非常大(4200),并且来自DSSP数据库的样本数量也非常大(11600)。结果表明,PLs变量选择方法有效地解决了变量数为4200,样本数为11600的海量数据建模问题。

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