Prediction of protein structural classes by support vector machines
Prediction of protein structural classes by support vector machines
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DOI：
10.1016/s0097-8485(01)00113-9
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发表时间：
2002-02-01
期刊：
影响因子：
--
通讯作者：
Chou, KC
中科院分区：
文献类型：
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作者：
Cai, YD;Liu, XJ;Chou, KC
在本文中，我们应用一种新的机器学习方法（称为支持向量机）来预测蛋白质结构类别。支持向量机方法是基于从 SCOP 导出的数据库来执行的，该数据库基于已知结构的域和进化关系以及控制其 3D 结构的原理。结果，获得了较高的自洽率和折刀测试率。这表明蛋白质的结构类别与其氨基酸组成密切相关，支持向量机可以作为预测蛋白质结构类别的强大计算工具。 (C) 2002 Elsevier Science Ltd. 保留所有权利。
In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino acid composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins. (C) 2002 Elsevier Science Ltd. All rights reserved.