Predicting O-glycosylation sites in mammalian proteins by using SVMs
Predicting O-glycosylation sites in mammalian proteins by using SVMs
复制标题
DOI：
10.1016/j.compbiolchem.2006.02.002
复制
发表时间：
2006-06-01
影响因子：
3.1
通讯作者：
Li, Yixue
中科院分区：
文献类型：
--
作者：
Li, Sujun;Liu, Boshu;Li, Yixue
O-糖基化是最重要、最常见、最复杂的翻译后修饰之一。这种修饰可以激活和影响蛋白质功能。在这里，我们提出了三个支持向量机模型的基础上的物理属性，0/1系统，并结合上述两个功能的系统。三种模型的预测精度分别达到0.82、0.85和0.85。三种支持向量机方法的精度进行了评估“留一法”交叉验证。这种方法提供了一个有用的工具，以帮助确定在哺乳动物蛋白质中的O-糖基化位点。在线预测网络服务器可在http://www.biosino.org/Oglyc上获得。(c)2006爱思唯尔有限公司保留所有权利。
O-glycosylation is one of the most important, frequent and complex post-translational modifications. This modification can activate and affect protein functions. Here, we present three support vector machines models based on physical properties, 0/1 system, and the system combining the above two features. The prediction accuracies of the three models have reached 0.82, 0.85 and 0.85, respectively. The accuracies of the three SVMs methods were evaluated by 'leave-one-out' cross validation. This approach provides a useful tool to help identify the O-glycosylation sites in mammalian proteins. An online prediction web server is available at http://www.biosino.org/Oglyc. (c) 2006 Elsevier Ltd. All rights reserved.