ArticleScientific reports2022
Multimodal deep learning applied to classify healthy and disease states of human microbiome.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fecal microbiota transplantation treatment of autoimmune-mediated type 1 diabetes: A systematic review.Frontiers in cellular and infection microbiology · 2022Pooled it
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Adaptive graph learning of microbial phylogeny enables accurate and interpretable microbiome-based host phenotype prediction.Applied and environmental microbiology · 2026Article
- Building a Global Research Network for Fair, Accountable, Interpretable, and Responsible AI in Emergency Care: Protocol for a FAIR-EC Study.JMIR research protocols · 2026Article
- Prevalence aware feature selection improves biomarker identification in microbiome studies.Bioinformatics (Oxford, England) · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Multimodal deep learning for inflammatory bowel disease: a new frontier in cellular and molecular biomarker discovery to clinical translation.Journal of biological engineering · 2026Review
- Review
- Probiotic supplementation as a nutritional strategy for the prevention and management of sarcopenia in older adults.Frontiers in cellular and infection microbiology · 2026Article
- Artificial Intelligence-Driven Food Safety: Decoding Gut Microbiota-Mediated Health Effects of Non-Microbial Contaminants.Foods (Basel, Switzerland) · 2025Review
- Review
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- Research on the method of constructing a knowledge map of relay protection device faults oriented to textual features.Scientific reports · 2025Article
- Current status and future perspectives of multi-modal bacteria-based cancer therapies.Clinical and translational medicine · 2025Review
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- MSFT-transformer: a multistage fusion tabular transformer for disease prediction using metagenomic data.Briefings in bioinformatics · 2025Article
- Effects of snake fungal disease (ophidiomycosis) on the skin microbiome across two major experimental scales.Conservation biology : the journal of the Society for Conservation Biology · 2025Article
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- Brain age mediates gut microbiome dysbiosis-related cognition in older adults.Alzheimer's research & therapy · 2025Article
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Metagenomic sequencing methods provide considerable genomic information regarding human microbiomes, enabling us to discover and understand microbial diseases. Compositional differences have been reported between patients and healthy people, which could be used in the diagnosis of patients. Despite significant progress in this regard, the accuracy of these tools needs to be improved for applications in diagnostics and therapeutics. MDL4Microbiome, the method developed herein, demonstrated high accuracy in predicting disease status by using various features from metagenome sequences and a multimodal deep learning model. We propose combining three different features, i.e., conventional taxonomic profiles, genome-level relative abundance, and metabolic functional characteristics, to enhance classification accuracy. This deep learning model enabled the construction of a classifier that combines these various modalities encoded in the human microbiome. We achieved accuracies of 0.98, 0.76, 0.84, and 0.97 for predicting patients with inflammatory bowel disease, type 2 diabetes, liver cirrhosis, and colorectal cancer, respectively; these are comparable or higher than classical machine learning methods. A deeper analysis was also performed on the resulting sets of selected features to understand the contribution of their different characteristics. MDL4Microbiome is a classifier with higher or comparable accuracy compared with other machine learning methods, which offers perspectives on feature generation with metagenome sequences in deep learning models and their advantages in the classification of host disease status.
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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.