Learning to discriminate between ligand-bound and disulfide-bound cysteines
Learning to discriminate between ligand-bound and disulfide-bound cysteines
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DOI：
10.1093/protein/gzh042
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发表时间：
2004-04-01
影响因子：
2.4
通讯作者：
Frasconi, P
中科院分区：
文献类型：
--
作者：
Passerini, A;Frasconi, P
我们提出了一种机器学习方法来区分参与配体结合的半胱氨酸和形成二硫键的半胱氨酸。我们的方法使用多个比对配置文件的窗口来表示每个实例，并使用具有多项式核的支持向量机作为学习算法。我们还报告了两个新的核函数相似矩阵的基础上获得的结果。实验结果表明，绑定类型可以预测在显着更高的精度比使用PROSITE模式。
We present a machine learning method to discriminate between cysteines involved in ligand binding and cysteines forming disulfide bridges. Our method uses a window of multiple alignment profiles to represent each instance and support vector machines with a polynomial kernel as the learning algorithm. We also report results obtained with two new kernel functions based on similarity matrices. Experimental results indicate that binding type can be predicted at significantly higher accuracy than using PROSITE patterns.