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

Background: Protein 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 anal...

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Bibliographic Details
Main Authors:	Li, W. (Author), Liu, Y. (Author), Wang, H. (Author), Wang, X. (Author), Wei, S. (Author), Xu, Y. (Author), Yang, Y. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	algorithm Algorithms Amino acids Correlation coefficient Correlation methods Decision trees Deep learning Forecasting Generative adversarial networks genetics lysine Lysine machine learning Machine Learning metabolism Pearson correlation coefficients Post-translational modification Post-translational modifications Prediction and analysis Predictive performance protein protein processing Protein Processing, Post-Translational Protein sequence data Proteins Proteomics Proteomics technology Pulse width modulation Random forest Random forest algorithm
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Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

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