N-GlycoGo: Predicting Protein N-Glycosylation Sites on Imbalanced Data Sets by Using Heterogeneous and Comprehensive Strategy

doi:10.1109/ACCESS.2020.3022629

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/24469

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/24469

Title:	N-GlycoGo: Predicting Protein N-Glycosylation Sites on Imbalanced Data Sets by Using Heterogeneous and Comprehensive Strategy
Authors:	Chien, CH;Chang, CC;Lin, SH;Chen, CW;Chang, ZH;Chu, YW
Keywords:	Proteins;Amino acids;Tools;Predictive models;Mice;Training;Encoding;Ensemble learning;machine learning;N-linked glycosylation
Date:	2020
Issue Date:	2022-08-09T08:02:50Z (UTC)
Publisher:	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
ISSN:	2169-3536
Abstract:	Glycosylation is the most complex post-modification effect of proteins. It participates in many biological processes in the human body and is closely related to many disease states. Among them, N-linked glycosylation is the most contained glycosylation data. However, the current N-linked glycosylation prediction tool does not take into account the serious imbalance between positive and negative data. In this study, we used protein sequence and amino acid characteristics to construct an N-linked glycosylation prediction model called N-GlycoGo. Based on sequence, structure, and function, 11 heterogeneous features were encoded. Further, XGBoost was selected for modeling. Finally, independent testing of human and mouse prediction models showed that N-GlycoGo is superior to other tools with Matthews correlation coefficient (MCC) values of 0.397 and 0.719, respectively, which is higher than other glycosylation site prediction tools. We have developed a fast and accurate prediction tool, N-GlycoGo, for N-linked glycosylation. N-GlycoGo is available at http://ncblab.nchu.edu.tw/n-glycogo/.
URI:	http://dx.doi.org/10.1109/ACCESS.2020.3022629 https://www.webofscience.com/wos/woscc/full-record/WOS:000572917800001 https://ir.csmu.edu.tw:8080/handle/310902500/24469
Relation:	IEEE ACCESS ,2020 ,v8 ,p165944-165950
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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