ArticleBMC bioinformatics2023
Mdwgan-gp: data augmentation for gene expression data based on multiple discriminator WGAN-GP.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- In silico generation of gene expression profiles using diffusion models.BMC bioinformatics · 2026Article
- ACmix-Swin Deep Learning of 4-Day-OldGenes · 2025Article
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Phenotype driven data augmentation methods for transcriptomic data.Bioinformatics advances · 2025Article
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6 authors.
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Abstract
backgroundAlthough gene expression data play significant roles in biological and medical studies, their applications are hampered due to the difficulty and high expenses of gathering them through biological experiments. It is an urgent problem to generate high quality gene expression data with computational methods. WGAN-GP, a generative adversarial network-based method, has been successfully applied in augmenting gene expression data. However, mode collapse or over-fitting may take place for small training samples due to just one discriminator is adopted in the method.
resultsIn this study, an improved data augmentation approach MDWGAN-GP, a generative adversarial network model with multiple discriminators, is proposed. In addition, a novel method is devised for enriching training samples based on linear graph convolutional network. Extensive experiments were implemented on real biological data.
conclusionsThe experimental results have demonstrated that compared with other state-of-the-art methods, the MDWGAN-GP method can produce higher quality generated gene expression data in most cases.
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