LipoSVM: Prediction of Lysine Lipoylation in Proteins based on the Support Vector Machine

doi:10.2174/1389202919666191014092843

	LipoSVM: Prediction of Lysine Lipoylation in Proteins based on the Support Vector Machine
	Wu, Meiqi 1; Lu, Pengchao 2; Yang, Yingxi 3; Liu, Liwen 1; Wang, Hui 4; Xu, Yan 1; Chu, Jixun 1
	2019
发表期刊	CURRENT GENOMICS
ISSN	1389-2029
卷号	20 期号:5 页码:362-370
摘要	Background: Lysine lipoylation which is a rare and highly conserved post-translational modification of proteins has been considered as one of the most important processes in the biological field. To obtain a comprehensive understanding of regulatory mechanism of lysine lipoylation, the key is to identify lysine lipoylated sites. The experimental methods are expensive and laborious. Due to the high cost and complexity of experimental methods, it is urgent to develop computational ways to predict lipoylation sites. Methodology: In this work, a predictor named LipoSVM is developed to accurately predict lipoylation sites. To overcome the problem of an unbalanced sample, synthetic minority over-sampling technique (SMOTE) is utilized to balance negative and positive samples. Furthermore, different ratios of positive and negative samples are chosen as training sets. Results: By comparing five different encoding schemes and five classification algorithms, LipoSVM is constructed finally by using a training set with positive and negative sample ratio of 1:1, combining with position-specific scoring matrix and support vector machine. The best performance achieves an accuracy of 99.98% and AUC 0.9996 in 10-fold cross-validation. The AUC of independent test set reaches 0.9997, which demonstrates the robustness of LipoSVM. The analysis between lysine lipoylation and non-lipoylation fragments shows significant statistical differences. Conclusion: A good predictor for lysine lipoylation is built based on position-specific scoring matrix and support vector machine. Meanwhile, an online webserver LipoSVM can be freely downloaded from https://github.com/stars20180811/LipoSVM.
关键词	Lysine lipoylation prediction amino acids support vector machine post-translational modifications scoring matrix
DOI	10.2174/1389202919666191014092843
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[11671032]
WOS研究方向	Biochemistry & Molecular Biology ; Genetics & Heredity
WOS类目	Biochemistry & Molecular Biology ; Genetics & Heredity
WOS记录号	WOS:000500783300006
出版者	BENTHAM SCIENCE PUBL LTD
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/14935
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chu, Jixun
作者单位	1.Univ Sci & Technol Beijing, Dept Appl Math, Beijing 100083, Peoples R China 2.China Petr Pipeline Engn Co Ltd, Equipment Leasing Co, Langfang City 065000, Hebei, Peoples R China 3.Hong Kong Univ Sci & Technol, Dept Chem & Biol Engn, Hong Kong, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wu, Meiqi,Lu, Pengchao,Yang, Yingxi,et al. LipoSVM: Prediction of Lysine Lipoylation in Proteins based on the Support Vector Machine[J]. CURRENT GENOMICS,2019,20(5):362-370.
APA	Wu, Meiqi.,Lu, Pengchao.,Yang, Yingxi.,Liu, Liwen.,Wang, Hui.,...&Chu, Jixun.(2019).LipoSVM: Prediction of Lysine Lipoylation in Proteins based on the Support Vector Machine.CURRENT GENOMICS,20(5),362-370.
MLA	Wu, Meiqi,et al."LipoSVM: Prediction of Lysine Lipoylation in Proteins based on the Support Vector Machine".CURRENT GENOMICS 20.5(2019):362-370.