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A support vector machine-based method for predicting chemokine receptors types

机译:基于支持向量机的趋化因子受体类型预测方法

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摘要

Chemokine receptors represent a prime target for the development of novel therapeutic strategies in a variety of disease processes. The prediction of interesting proteins types by computational methods can provide new clues in functional studies of uncharacterized proteins without performing extensive experiments. Support vector machine (SVM) is a new kind of approach to supervised pattern classification that has been successfully applied to a wide range of computational biology fields. In this study, a SVM classifier was implemented to predict two main types of chemokine receptors based solely on amino acid composition and associated physicochemical properties. The performance on the tree kernel method we developed is comparable to that of other kernels while giving distinct advantages when evaluated through 10-fold cross-validation technique, indicating the current approach may serve as a useful tool for further investigating the processes of cell molecular mechanism of this important family. The experimental results also show that the features and the classifiers in detecting chemokine receptors types are effective.
机译:趋化因子受体代表了在各种疾病过程中开发新型治疗策略的主要目标。通过计算方法预测感​​兴趣的蛋白质类型可以为未表征的蛋白质的功能研究提供新的线索,而无需进行广泛的实验。支持向量机(SVM)是一种新型的监督模式分类方法,已成功地应用于各种计算生物学领域。在这项研究中,仅基于氨基酸组成和相关的理化特性,就采用了SVM分类器来预测两种主要类型的趋化因子受体。我们开发的树核方法的性能可与其他核相比,但通过10倍交叉验证技术进行评估时具有明显的优势,这表明当前的方法可作为进一步研究细胞分子机制过程的有用工具重要家庭实验结果还表明,检测趋化因子受体类型的特征和分类器是有效的。

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