ArticleBioinformatics (Oxford, England)2023
DeepMicroGen: a generative adversarial network-based method for longitudinal microbiome data imputation.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- LGTM: Gaussian process modulated neural topic modeling for longitudinal microbiome.Gut microbes · 2026Article
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models.BMC bioinformatics · 2026Article
- DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data.Computational and structural biotechnology journal · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- A systematic longitudinal study of microbiome: integrating temporal-spatial dimensions with causal and deep learning models.BMC genomics · 2025Article
- Review
- Article
- Diffusion model for imputing time-series gut microbiome profiles using phylogenetic information and metadata integration.Bioinformatics advances · 2025Article
- Article
- A Knowledge-Guided Large Language Model Framework for Microbiome-Based Disease Diagnosis.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- MIDASim: a fast and simple simulator for realistic microbiome data.Microbiome · 2024Article
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
- Detracking Autoencoding Conditional Generative Adversarial Network: Improved Generative Adversarial Network Method for Tabular Missing Value Imputation.Entropy (Basel, Switzerland) · 2024Article
- phylaGAN: data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data.Bioinformatics (Oxford, England) · 2024Article
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- Data pre-processing for analyzing microbiome data - A mini review.Computational and structural biotechnology journal · 2023Review
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4 authors.
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Abstract
motivationThe human microbiome, which is linked to various diseases by growing evidence, has a profound impact on human health. Since changes in the composition of the microbiome across time are associated with disease and clinical outcomes, microbiome analysis should be performed in a longitudinal study. However, due to limited sample sizes and differing numbers of timepoints for different subjects, a significant amount of data cannot be utilized, directly affecting the quality of analysis results. Deep generative models have been proposed to address this lack of data issue. Specifically, a generative adversarial network (GAN) has been successfully utilized for data augmentation to improve prediction tasks. Recent studies have also shown improved performance of GAN-based models for missing value imputation in a multivariate time series dataset compared with traditional imputation methods.
resultsThis work proposes DeepMicroGen, a bidirectional recurrent neural network-based GAN model, trained on the temporal relationship between the observations, to impute the missing microbiome samples in longitudinal studies. DeepMicroGen outperforms standard baseline imputation methods, showing the lowest mean absolute error for both simulated and real datasets. Finally, the proposed model improved the predicted clinical outcome for allergies, by providing imputation for an incomplete longitudinal dataset used to train the classifier. AVAILABILITY AND IMPLEMENTATION: DeepMicroGen is publicly available at https://github.com/joungmin-choi/DeepMicroGen.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.