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A deep learning method to more accurately recall known lysine acetylation sites

文献类型：期刊论文

作者Wu, Meiqi3; Yang, Yingxi3; Wang, Hui2; Xu, Yan1,3
刊名BMC BIOINFORMATICS
出版日期2019-01-23
卷号20页码:11
关键词Lysine acetylation PTMs Deep learning
ISSN号1471-2105
DOI10.1186/s12859-019-2632-9
英文摘要BackgroundLysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine learning has some disadvantages. For instance, it is not as effective as deep learning for processing big data.ResultsIn this work, a novel predictor named DeepAcet was developed to predict acetylation sites. Six encoding schemes were adopted, including a one-hot, BLOSUM62 matrix, a composition of K-space amino acid pairs, information gain, physicochemical properties, and a position specific scoring matrix to represent the modified residues. A multilayer perceptron (MLP) was utilized to construct a model to predict lysine acetylation sites in proteins with many different features. We also integrated all features and implemented the feature selection method to select a feature set that contained 2199 features. As a result, the best prediction achieved 84.95% accuracy, 83.45% specificity, 86.44% sensitivity, 0.8540 AUC, and 0.6993 MCC in a 10-fold cross-validation. For an independent test set, the prediction achieved 84.87% accuracy, 83.46% specificity, 86.28% sensitivity, 0.8407 AUC, and 0.6977 MCC.ConclusionThe predictive performance of our DeepAcet is better than that of other existing methods. DeepAcet can be freely downloaded from https://github.com/Sunmile/DeepAcet.
资助项目Natural Science Foundation of China[11671032] ; Fundamental Research Funds for the Central Universities[FRF-TP-17-024A2] ; National traditional Medicine Clinical Research Base Business Construction Special Topics[JDZX2015299]
WOS研究方向Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000456522700001
出版者BMC
源URL[http://119.78.100.204/handle/2XEOYT63/3472]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xu, Yan
作者单位1.Univ Sci & Technol Beijing, Beijing Key Lab Magnetophotoelect Composite & Int, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Univ Sci & Technol Beijing, Dept Informat & Comp Sci, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Meiqi,Yang, Yingxi,Wang, Hui,et al. A deep learning method to more accurately recall known lysine acetylation sites[J]. BMC BIOINFORMATICS,2019,20:11.
APA Wu, Meiqi,Yang, Yingxi,Wang, Hui,&Xu, Yan.(2019).A deep learning method to more accurately recall known lysine acetylation sites.BMC BIOINFORMATICS,20,11.
MLA Wu, Meiqi,et al."A deep learning method to more accurately recall known lysine acetylation sites".BMC BIOINFORMATICS 20(2019):11.

入库方式： OAI收割

来源：计算技术研究所

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