CPSNF:具有新特征的蛋白质结构分类

Hong-Xuan Hua
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引用次数: 0

摘要

蛋白质结构在生物学的许多领域起着关键作用。然而,从蛋白质序列中鉴定蛋白质结构类型似乎是一个具有挑战性的问题。在本研究中,提出了几种新的重构特征,并将其作为处理机器学习问题的特征。为了展示这些特征的性能,我们在1189和25PDB两个基准数据集中使用了10倍。我们提出的特征可以有效地处理四种类型的蛋白质三级结构,而不是其他的状态艺术方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CPSNF: Classification of Protein Structure with Novel Features
Protein structures play key roles in many fields of biology. However, identification of protein structural types from protein sequences seems to be a challenge issue. In this study, several novel reconstructed features have been proposed and employed to be the features to deal with the machine learning issue. So as to demonstrate the performance of these features, 10-fold has been utilized in two benchmark datasets, including 1189 and 25PDB. Our proposed features can be effective to deal with the four types of protein tertiary structure than other art-of-the-state methods.
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