运用最大相关最小冗余法和迭代特征选择法预测与分析蛋白质糖基化位点
Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods.
作者信息
Liu Yan, Gu Wenxiang, Zhang Wenyi, Wang Jianan
机构信息
College of Mathematics and Statistics, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China ; College of Computer Science and Information Technology, Northeast Normal University, 2555 Jingyue Street, Changchun 130117, China.
College of Computer Science and Information Technology, Northeast Normal University, 2555 Jingyue Street, Changchun 130117, China ; Institute of Applied Mathematics and Intelligent Systems, Changchun Architecture & Civil Engineering College, Changchun 130607, China.
出版信息
Biomed Res Int. 2015;2015:561547. doi: 10.1155/2015/561547. Epub 2015 Apr 15.
Glycation is a nonenzymatic process in which proteins react with reducing sugar molecules. The identification of glycation sites in protein may provide guidelines to understand the biological function of protein glycation. In this study, we developed a computational method to predict protein glycation sites by using the support vector machine classifier. The experimental results showed that the prediction accuracy was 85.51% and an overall MCC was 0.70. Feature analysis indicated that the composition of k-spaced amino acid pairs feature contributed the most for glycation sites prediction.
糖基化是一种蛋白质与还原糖分子发生反应的非酶促过程。蛋白质中糖基化位点的鉴定可为理解蛋白质糖基化的生物学功能提供指导。在本研究中，我们开发了一种利用支持向量机分类器预测蛋白质糖基化位点的计算方法。实验结果表明，预测准确率为85.51%，总体马修斯相关系数为0.70。特征分析表明，k间隔氨基酸对特征的组成对糖基化位点预测贡献最大。
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