Huang Jinyan
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Int J Bioinform Res Appl. 2013;9(3):271-84. doi: 10.1504/IJBRA.2013.053608.
Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental identification of phosphorylation sites is labour-intensive and often limited by the availability and optimisation of enzymatic reaction. An ensemble learning approach that combines different encodings using a meta-learner was developed which was catalyzed by four protein kinase families and three residues. A predictor is constructed to predict the true and false phosphorylation sites based on Support Vector Machines (SVM), and knowledge based encoding method is used for amino sequences. Different encoding methods catch different aspects of amino sequences feature. The stacking SVM approach was applied to combine these aspects and improved both sensitivity and specificity.
蛋白质磷酸化在大多数细胞调节途径中起着基础性作用。磷酸化位点的实验鉴定工作强度大，且常常受到酶促反应可用性和优化的限制。开发了一种集成学习方法，该方法使用元学习器结合不同的编码方式，由四个蛋白激酶家族和三个残基催化。构建了一个基于支持向量机（SVM）的预测器来预测真假磷酸化位点，并将基于知识的编码方法用于氨基酸序列。不同的编码方法捕捉氨基酸序列特征的不同方面。应用堆叠支持向量机方法来整合这些方面，提高了敏感性和特异性。
