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

ABSTRACT:

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 analysis of PTMs in proteins.

Method

We 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 inf

SUBMITTER: Yang Y 

PROVIDER: S-EPMC8010967 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature