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。
The process is carried out computationally in a high throughput manner by mining the ever-expanding databases of protein sequences of all organisms, especially human.
该方法通过挖掘不断膨胀的所有生物、特别是人类的蛋白质序列数据库，以高通量的方式在计算机上进行。
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。
Therefore, a unified standard of protein-protein interaction databases is in urgent need for collecting, collating the existing data and mining for useful information from them.
因此，目前亟需一个统一且规范的蛋白质相互作用数据库系统来收集和管理这些数据，并从已有的数据中挖掘有用信息。
Therefore, a unified standard of protein-protein interaction databases is in urgent need for collecting, collating the existing data and mining for useful information from them.
因此，目前亟需一个统一且规范的蛋白质相互作用数据库系统来收集和管理这些数据，并从已有的数据中挖掘有用信息。
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