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Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

文献类型：期刊论文

作者Yang, Yingxi2; Wang, Hui1; Li, Wen2; Wang, Xiaobo2; Wei, Shizhao3; Liu, Yulong3; Xu, Yan2
刊名BMC BIOINFORMATICS
出版日期2021-03-31
卷号22期号:1页码:17
关键词Post-translational modification Deep learning Generative adversarial networks Random forest
ISSN号1471-2105
DOI10.1186/s12859-021-04101-y
英文摘要BackgroundProtein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins.MethodWe proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories.ResultsIn the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN.ConclusionsThe CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
资助项目Natural Science Foundation of China[12071024] ; Ministry of Science and Technology of China[2019AAA0105103]
WOS研究方向Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000636449300003
出版者BMC
源URL[http://119.78.100.204/handle/2XEOYT63/16734]  
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.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China
2.Univ Sci & Technol Beijing, Dept Informat & Comp Sci, Beijing 100083, Peoples R China
3.China Elect Technol Grp Corp, Res Inst 15, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Yingxi,Wang, Hui,Li, Wen,et al. Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks[J]. BMC BIOINFORMATICS,2021,22(1):17.
APA Yang, Yingxi.,Wang, Hui.,Li, Wen.,Wang, Xiaobo.,Wei, Shizhao.,...&Xu, Yan.(2021).Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC BIOINFORMATICS,22(1),17.
MLA Yang, Yingxi,et al."Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks".BMC BIOINFORMATICS 22.1(2021):17.

入库方式： OAI收割

来源：计算技术研究所

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