Protein function classification via support vector machine approach
Protein function classification via support vector machine approach
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DOI：
10.1016/s0025-5564(03)00096-8
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发表时间：
2003-10-01
影响因子：
4.3
通讯作者：
Chen, YZ
中科院分区：
文献类型：
--
作者：
Cai, CZ;Wang, WL;Chen, YZ
支持向量机（SVM）被引入作为一种方法，用于蛋白质的功能区分类的分类。对许多蛋白质类进行研究，包括RNA结合蛋白;蛋白质同源二聚体，负责药物吸收的蛋白质，参与药物分布和排泄的蛋白质，以及药物代谢酶。这些蛋白质类的分类的测试准确度被发现是在84- 96%的范围内。这表明支持向量机在蛋白质功能分类中的有效性及其在蛋白质功能预测中的潜在应用。(C)2003年爱思唯尔公司All rights reserved.
Support vector machine (SVM) is introduced as a method for the classification of proteins into functionally distinguished classes. Studies are conducted on a number of protein classes including RNA-binding proteins; protein homodimers, proteins responsible for drug absorption, proteins involved in drug distribution and excretion, and drug metabolizing enzymes. Testing accuracy for the classification of these protein classes is found to be in the range of 84-96%. This suggests the usefulness of SVM in the classification of protein functional classes and its potential application in protein function prediction. (C) 2003 Elsevier Inc. All rights reserved.