基于模糊支持向量机的赖氨酸糖化位点预测

宋一明; 鞠哲; 张万里

doi:10.3969/j.issn.2095-1248.2023.03.008

沈阳航空航天大学学报 ›› 2023, Vol. 40 ›› Issue (3) : 63 -70. DOI: 10.3969/j.issn.2095-1248.2023.03.008

信息科学与工程

基于模糊支持向量机的赖氨酸糖化位点预测

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Prediction of lysine glycation sites based on fuzzy support vector machine

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摘要

能否准确识别糖化位点对理解糖化的分子机制有着重要意义。传统的实验方法工作量大、耗时长，因此迫切需要开发计算辅助方法来预测糖化位点。设计了一种新的模糊支持向量机算法，该算法放大了重要特征与弱相关特征间的权重之差，同时考虑了样本内部的分布情况，能够有效地处理糖化修饰位点预测中含有噪声数据的问题。基于所提出的模糊支持向量机算法结合双剖面贝叶斯（Bi-Profile Bayes，BPB）特征提取方法构建了一个新的赖氨酸糖化位点的模型——FSVM_GlySite。十折交叉验证结果表明，FSVM_GlySite的预测效果优于现有的几种糖基化位点预测器。

Abstract

Accurate identification of glycation sites is of great significance to understanding the molecular mechanism of glycation. Because of the heavy workload and time consuming of traditional experimental methods， it is urgent to develop computational auxiliary methods to predict the glycation sites. A new fuzzy support vector machine algorithm was designed， which magnified the weight difference between the important features and the weak correlation features， and also considered the distribution within the sample. The algorithm could effectively deal with the problem of noise data in the prediction of glycation modification sites. Based on the proposed fuzzy support vector machine algorithm and Bi-Profile Bayes（BPB） feature extraction method， a new lysine glycation site model， FSVM_GlySite， was constructed. The results of 10-fold cross-validation showes that the prediction effect of FSVM_GlySite is better than that of several existing glycation site predictors.