NDLI: A novel method for predicting protein phosphorylation via site-modification network profiles

Content Provider	IEEE Xplore Digital Library
Author	Qin, Zijun Wang, Minghui Jiang, Yujie Xu, Xiaoyi Feng, Huanqing Li, Ao
Copyright Year	2015
Description	Author affiliation: School of Information Science and Technology, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China (Qin, Zijun; Wang, Minghui; Jiang, Yujie; Xu, Xiaoyi; Feng, Huanqing; Li, Ao)
Abstract	Protein phosphorylation, one of the most important types of post-translational modifications (PTMs), participates in multiple cellular processes. Accurate prediction on phosphorylaiton sites has become necessary, as many modifications are related to diseases and used as biomarkers. Currently a number of computational approaches only establish prediction models on sequence information. In this study, site-modification network (SMNet) profiles are proposed to enhance the prediction performance, which reflect information among in situ PTMs. In addition, a two-step algorithm that incorporates SVM with feature selection is adopted. To further demonstrate the method, we compare it with PPSP and GPS 3. 0, finally the results indicate that SMNet profiles effectively improve the performance on predicting phosphorylation sites.
Starting Page	458
Ending Page	462
File Size	351265
Page Count	5
File Format	PDF
e-ISBN	9781509000227
DOI	10.1109/BMEI.2015.7401548
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Support vector machines Training Protein engineering Phosphorylation Predictive models SMNet profiles SVM feature selection Global Positioning System Testing
Content Type	Text
Resource Type	Article

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