Prediction of protein structural class using a complexity-based distance measure
Prediction of protein structural class using a complexity-based distance measure
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DOI：
10.1007/s00726-009-0276-1
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发表时间：
2010-03-01
期刊：
影响因子：
3.5
通讯作者：
Wang, Jun
中科院分区：
文献类型：
--
作者：
Liu, Taigang;Zheng, Xiaoqi;Wang, Jun
结构类知识在理解蛋白质折叠模式中起着重要的作用。因此，有必要开发有效、可靠的蛋白质结构类预测计算方法。为此，我们提出了一种新的方法，称为NN-CDM，一种基于复杂度的距离度量的最近邻分类器。该方法不像以前那样从蛋白质序列中提取特征，而是通过符号序列的复杂度来直接评估每对蛋白质序列之间的距离。然后采用最近邻分类器作为预测引擎。为了验证该方法的性能，在多个基准数据集上进行了交叉验证测试。结果表明，该方法比一些经典方法具有更高的预测精度。
Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach achieves a high prediction accuracy over some classical methods.