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

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
Online Access:View Fulltext in Publisher

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