Prediction of the O-glycosylation Sites in Protein by Layered Neural Networks and Support Vector Machines
Prediction of the O-glycosylation Sites in Protein by Layered Neural Networks and Support Vector Machines
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DOI：
10.1007/11893004_122
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发表时间：
2006-10
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影响因子：
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通讯作者：
I. Nishikawa;Hirotaka Sakamoto;Ikue Nouno;T. Iritani;K. Sakakibara;M. Ito
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文献类型：
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作者：
I. Nishikawa;Hirotaka Sakamoto;Ikue Nouno;T. Iritani;K. Sakakibara;M. Ito
O-糖基化是哺乳动物蛋白质糖基化的主要类型之一，其具有丝氨酸或苏氨酸特异性，但任何共有序列仍未知。本文采用分层神经网络和支持向量机来预测O-糖基化位点。固定大小窗口内蛋白质序列的三种编码被用作网络的输入，即区分所有20个氨基酸残基的稀疏编码、5字母编码和水蛋白编码。在神经网络中，一个输出单元可以预测丝氨酸或苏氨酸的特定位点是否被糖基化，而 SVM 则分为两类。性能通过马修斯相关系数来评估。神经网络的初步结果表明，与水病编码相比，稀疏编码和 5 字母编码具有更好的性能，而根据窗口大小的改进在一定程度上受到 SVM 的限制。
O-glycosylation is one of the main types of the mammalian protein glycosylation, which is serine or threonine specific, though any consensus sequence is still unknown. In this paper, a layered neural network and a support vector machine are used for the prediction ofO-glycosylation sites. Three types of encoding for a protein sequence within a fixed size window are used as the input to the network, that is, a sparse coding which distinguishes all 20 amino acid residues, 5-letter coding and hydropathy coding. In the neural network, one output unit gives the prediction whether a particular site of serine or threonine is glycosylated, while SVM classifies into the 2 classes. The performance is evaluated by the Matthews correlation coefficient. The preliminary results on the neural network show the better performance of the sparse and 5-letter codings compared with the hydropathy coding, while the improvement according to the window size is shown to be limited to a certain extent by SVM.