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Prediction of human disease-specific phosphorylation sites with combined feature selection approach and support vector machine

机译：结合特征选择方法和支持向量机预测人类疾病特异性磷酸化位点

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Phosphorylation is a crucial post translational modification, which regulates almost all cellular process in life. It has long been recognized that protein phosphorylation has close relationship with diseases, and therefore many researches are undertaken to predict phosphorylation sites for disease treatment and drug design. However, despite the success achieved by these approaches, no method focuses on disease-associated phosphorylation sites prediction. Herein, for the first time we propose a novel approach that is specially designed to identify disease-specific phosphorylation sites based on SVM. Human disease-associated phosphorylation data is extracted from PhosphoSitePlus database and local sequences are derived for training. To take full advantage of sequence information, a combined feature selection method-based SVM (CFS-SVM) that incorporates mRMR filtering process and forward feature selection process is developed. With CFS-SVM, we successfully predict disease-specific phosphorylation sites. Performance evaluation shows that CFS-SVM is significantly better than the widely used classifiers, including Bayesian decision theory and k nearest neighbour. With the extremely high specificity of 99%, CFS-SVM can still achieve a high sensitivity. Besides, the analysis of corresponding kinases and selected features also shed light on understanding of the potential mechanism of disease-phosphorylation relationships and guide further experimental validations.
机译：磷酸化是至关重要的翻译后修饰，它调节生活中几乎所有的细胞过程。长期以来，人们已经认识到蛋白质的磷酸化与疾病有着密切的关系，因此进行了许多研究来预测疾病治疗和药物设计中的磷酸化位点。然而，尽管通过这些方法获得了成功，但是没有方法专注于与疾病相关的磷酸化位点的预测。在此，我们首次提出了一种新颖的方法，该方法专门设计用于基于SVM识别疾病特异性的磷酸化位点。从PhosphoSitePlus数据库中提取与人类疾病相关的磷酸化数据，并导出局部序列进行训练。为了充分利用序列信息，开发了一种结合了mRMR过滤过程和前向特征选择过程的，基于特征选择方法的组合SVM（CFS-SVM）。借助CFS-SVM，我们可以成功预测疾病特异性的磷酸化位点。性能评估表明，CFS-SVM明显优于广泛使用的分类器，包括贝叶斯决策理论和k最近邻。凭借99％的极高特异性，CFS-SVM仍可实现高灵敏度。此外，对相应激酶和所选特征的分析也有助于了解疾病磷酸化关系的潜在机制，并指导进一步的实验验证。

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