However this process is quite tough. Here our statistical information database by data mining is used for predicting protein secondary structure.
本文中，我们利用数据挖掘得到的统计信息数据库对蛋白质的二级结构进行了预测。
Abstract : Massive data is accumulated in the aspects of genome, structure and function of protein.
摘要 ：基因组和蛋白质结构与功能方面已积累了海量数据。
This method does not rely on gene sequence or protein structure homologies, and it can be applied to any organism and a wide variety of experimental data sets.
这个方法不依赖于基因的序列或者是蛋白的同源结构，它能够适用于任何的生物体和大量的实验数据集。
The Protein data Bank is the single worldwide repository for the processing and distribution of 3-d biological macromolecular structure data.
蛋白质数据库是全球唯一一个贮存三维生物巨分子结构的处理与分布数据的储藏库。
Protein second structure data is chosen as main study object, and data mining and dynamic programming are applied to protein structure classification.
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。
In the case of a protein whose crystal structure and sequence are known, the PCDDB entry will be linked to the appropriate PDB and sequence data bank files, respectively.
在一个蛋白质的晶体结构和序列已知情况下，PCDDB条目将分别链接到合适的PDB和序列数据库文件。
In the case of a protein whose crystal structure and sequence are known, the PCDDB entry will be linked to the appropriate PDB and sequence data bank files, respectively.
在一个蛋白质的晶体结构和序列已知情况下，PCDDB条目将分别链接到合适的PDB和序列数据库文件。
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