期刊
MOLECULAR BIOSYSTEMS
卷 9, 期 11, 页码 2909-2913出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c3mb70326f
关键词
-
资金
- National Natural Science Foundation of China [31101671]
- Liaoning Provincial Natural Science Foundation of China [201102015]
- Fundamental Research Funds for the Central Universities [3132013332, 3132013093, DUT11RC(3)73]
O-GlcNAcylation is a ubiquitous post-translational modification of proteins that is involved in the majority of cellular processes and is associated with many diseases. To reduce the workload and increase the relevance of experimental identification of protein O-GlcNAcylation sites, O-GlcNAcPRED, a support vector machine (SVM)-based model, was developed to capture potential O-GlcNAcylation sites. By virtue of the novel adapted normal distribution bi-profile Bayes (ANBPB) feature extraction method, O-GlcNAcPRED yielded a sensitivity of 80.83%, a specificity of 78.17% and an accuracy of 79.50% in jackknife cross-validation experiments. In an independent test on 38 recently experimentally identified human O-GlcNAcylated proteins with 67 O-GlcNAcylation sites, O-GlcNAcPRED captured 26 proteins and 39 sites, clearly outperforming the existing predictors, YinOYang and O-GlcNAcscan.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据