ArticlePatterns (New York, N.Y.)2023
Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Fermented food microbiome: influence on oral and gut microbiota, and human health.Nature reviews. Microbiology · 2026Review
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- AI-empowered human microbiome research.Gut · 2026Review
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Predicting anti-CCP positivity and early rheumatoid arthritis onset from routine laboratory parameters: a SHAP-explained machine learning pipeline.Frontiers in medicine · 2026Article
- Review
- SHAP-based binarization enhances metataxonomic machine learning with application to gut microbiota of inflammatory bowel disease.Scientific reports · 2025Article
- Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives.Journal of microbiology and biotechnology · 2025Review
- Bioinformatic approaches to blood and tissue microbiome analyses: challenges and perspectives.Briefings in bioinformatics · 2025Review
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- Toward ordered -omics data science: Researchers on the magic of turning metagenomic chaos into image-like patterns.Patterns (New York, N.Y.) · 2023Article
- Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease.GigaScience · 2022Article
- Microbial Biomarkers in Liquid Biopsy for Cancer: An Overview and Future Directions.Cancer control : journal of the Moffitt Cancer CenterReview
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Authors and funding
4 authors.
Funding
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Abstract
Metagenomic analysis has been explored for disease diagnosis and biomarker discovery. Low sample sizes, high dimensionality, and sparsity of metagenomic data challenge metagenomic investigations. Here, an unsupervised microbial embedding, grouping, and mapping algorithm (MEGMA) was developed to transform metagenomic data into individualized multichannel microbiome 2D representation by manifold learning and clustering of microbial profiles (e.g., composition, abundance, hierarchy, and taxonomy). These 2D representations enable enhanced disease prediction by established ConvNet-based AggMapNet models, outperforming the commonly used machine learning and deep learning models in metagenomic benchmark datasets. These 2D representations combined with AggMapNet explainable module robustly identified more reliable and replicable disease-prediction microbes (biomarkers). Employing the MEGMA-AggMapNet pipeline for biomarker identification from 5 disease datasets, 84% of the identified biomarkers have been described in over 74 distinct works as important for these diseases. Moreover, the method also discovered highly consistent sets of biomarkers in cross-cohort colorectal cancer (CRC) patients and microbial shifts in different CRC stages.
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