ArticleGigaScience2021
MB-GAN: Microbiome Simulation via Generative Adversarial Network.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 41 citations in OpenAlex.
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Coenocline Simulation of Microbiome Samples: A Biologically Mechanistic Framework for Generating Ecologically Realistic Synthetic Datasets to Support Classification Method Evaluation.Pathogens (Basel, Switzerland) · 2026Article
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- MGM as a Large-Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- TaxaPLN: a taxonomy-aware augmentation strategy for microbiome-trait classification including metadata.BMC bioinformatics · 2025Article
- PhyloMix: enhancing microbiome-trait association prediction through phylogeny-mixing augmentation.Bioinformatics (Oxford, England) · 2025Article
- Article
- MeSS and assembly_finder: a toolkit for in silico metagenomic sample generation.Bioinformatics (Oxford, England) · 2024Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- MIDASim: a fast and simple simulator for realistic microbiome data.Microbiome · 2024Article
- BacteSign: Building a Findable, Accessible, Interoperable, and Reusable (FAIR) Database for Universal Bacterial Identification.Biosensors · 2024Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- phylaGAN: data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data.Bioinformatics (Oxford, England) · 2024Article
- Computational Study Protocol: Leveraging Synthetic Data to Validate a Benchmark Study for Differential Abundance Tests for 16S Microbiome Sequencing Data.F1000Research · 2024Article
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- DeepMicroGen: a generative adversarial network-based method for longitudinal microbiome data imputation.Bioinformatics (Oxford, England) · 2023Article
- AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data.GigaScience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundTrillions of microbes inhabit the human body and have a profound effect on human health. The recent development of metagenome-wide association studies and other quantitative analysis methods accelerate the discovery of the associations between human microbiome and diseases. To assess the strengths and limitations of these analytical tools, simulating realistic microbiome datasets is critically important. However, simulating the real microbiome data is challenging because it is difficult to model their correlation structure using explicit statistical models.
resultsTo address the challenge of simulating realistic microbiome data, we designed a novel simulation framework termed MB-GAN, by using a generative adversarial network (GAN) and utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from given microbial abundances and compute simulated abundances that are indistinguishable from them. In practice, MB-GAN showed the following advantages. First, MB-GAN avoids explicit statistical modeling assumptions, and it only requires real datasets as inputs. Second, unlike the traditional GANs, MB-GAN is easily applicable and can converge efficiently.
conclusionsBy applying MB-GAN to a case-control gut microbiome study of 396 samples, we demonstrated that the simulated data and the original data had similar first-order and second-order properties, including sparsity, diversities, and taxa-taxa correlations. These advantages are suitable for further microbiome methodology development where high-fidelity microbiome data are needed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.