WGAN-GP_Glu: A semi-supervised model based on double generator-Wasserstein GAN with gradient penalty algorithm for glutarylation site identification

• Published in: Computers in biology and medicine Vol. 184; p. 109328
• Main Authors: Ning, Qiao, Qi, Zedong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2025; Elsevier Limited
• Subjects: Algorithms; Amino acids; Annotations; Artificial neural networks; Cell cycle; Computational Biology - methods; Correlation coefficients; Discriminators; Feature extraction; Feature selection; Glutarates - chemistry; Glutarates - metabolism; Glutarylation site identification; Humans; Internal Medicine; Long short-term memory; Lysine; Machine learning; Modules; Multiple generators; Negative sampling; Neural networks; Noise generation; Other; Peptides; Post-translation; Predictions; Protein Processing, Post-Translational; Proteins; Semi-supervised learning; Semi-supervised model; Supervised Machine Learning; Multiple generators; Glutarylation site identification; Semi-supervised model; Negative sampling
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109328

As an important post-translational modification, glutarylation plays a crucial role in a variety of cellular functions. Recently, diverse computational methods for glutarylation site identification have been proposed. However, the class imbalance problem due to data noise and uncertainty of non-glutarylation sites remains a great challenge. In this article, we propose a novel semi-supervised learning algorithm, called WGAN-GP_Glu, for identifying reliable non-glutarylation lysine sites from those without glutarylation annotation. WGAN-GP_Glu method is a multi-module framework algorithm, which mainly includes a reliable negative sample selection module, a deep feature extraction module, and a glutarylation site prediction module. In reliable negative sample selection module, we design an improved method of Wasserstein GAN with Gradient Penalty (WGAN-GP), named ReliableWGAN-GP, including three parts, two generators G1, G2 and a discriminator D, which can select reliable non-glutarylation samples from a great number of unlabeled samples. Generator G1 is utilized to generate noise data from unlabeled samples. For generator G2, both the positive sample and the noise data are used as inputs to improve the discriminant capability of discriminator D. Then, convolutional neural network and bidirectional long short-term memory network combined with attention mechanism are utilized to extract deep features for glutarylation samples and reliable non-glutarylation samples. Finally, a glutarylation site prediction module based on the three-layer fully connected layer is designed to make class predictions for samples. The sensitivity, specificity, accuracy and Matthew correlation coefficient of WGAN-GP_Glu on the independent test data set reach 90.58 %, 95.82 %, 94.44 % and 0.8645, respectively, which surpassed the existing methods for glutarylation sites prediction. Therefore, WGAN-GP_Glu can serve as a powerful tool in identifying glutarylation sites and the ReliableWGAN-GP algorithm is effective in selecting reliable negative samples. The data and code are available at https://github.com/xbbxhbc/WGAN-GP_Glu.git. •We propose ReliableWGAN-GP, the improvement of WGAN-GPwith two generators and a discriminator, to solve the data imbalance problem.•The WGAN-GP_Glu is a semi-supervised learning algorithm integrating the ReliableWGAN-GP algorithm and multi-view feature encoding scheme.•CNN and Bi-LSTM combined with attention mechanism are utilized to extract deep features and direction-dependent features for samples.