计算机科学
支持向量机
鉴定(生物学)
分类器(UML)
特征选择
预测值
人工智能
数据挖掘
生物
医学
植物
内科学
作者
Xiaowei Zhao,Ning Qiao,Haiting Chai,Meiyue Ai,Zhiqiang Ma
标识
DOI:10.1016/j.jtbi.2015.06.026
摘要
As a widespread type of protein post-translational modification, O-GlcNAcylation plays crucial regulatory roles in almost all cellular processes and is related to some diseases. To deeply understand O-GlcNAcylated mechanisms, identification of substrates and specific O-GlcNAcylated sites is crucial. Experimental identification is expensive and time-consuming, so computational prediction of O-GlcNAcylated sites has considerable value. In this work, we developed a novel O-GlcNAcylated sites predictor called PGlcS (Prediction of O-GlcNAcylated Sites) by using k-means cluster to obtain informative and reliable negative samples, and support vector machines classifier combined with a two-step feature selection. The performance of PGlcS was evaluated using an independent testing dataset resulting in a sensitivity of 64.62%, a specificity of 68.4%, an accuracy of 68.37%, and a Matthew׳s correlation coefficient of 0.0697, which demonstrated PGlcS was very promising for predicting O-GlcNAcylated sites. The datasets and source code were available in Supplementary information.
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