ArticleGenome biology2019
MITRE: inferring features from microbiota time-series data linked to host status.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses 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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- A temporal-aware machine learning framework enables microbial community dynamics prediction with personalized precision.Microbiome · 2025Article
- MMETHANE: interpretable AI for predicting host status from microbial composition and metabolomics data.Microbiome · 2025Article
- Article
- 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
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- Succession of the multi-site microbiome along pancreatic ductal adenocarcinoma tumorigenesis.Frontiers in immunology · 2024Article
- Universal abundance fluctuations across microbial communities, tropical forests, and urban populations.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Influence of Maternal Breast Milk and Vaginal Microbiome on Neonatal Gut Microbiome: a Longitudinal Study during the First Year.Microbiology spectrum · 2023Article
- Overview of data preprocessing for machine learning applications in human microbiome research.Frontiers in microbiology · 2023Review
- A self-knowledge distillation-driven CNN-LSTM model for predicting disease outcomes using longitudinal microbiome data.Bioinformatics advances · 2023Article
- MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics.mSystems · 2022Article
- Microbial Dark Matter: from Discovery to Applications.Genomics, proteomics & bioinformatics · 2022Review
- Temporal dynamics of oropharyngeal microbiome among SARS-CoV-2 patients reveals continued dysbiosis even after Viral Clearance.NPJ biofilms and microbiomes · 2022Article
- Human disease prediction from microbiome data by multiple feature fusion and deep learning.iScience · 2022Article
- Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System.mSystems · 2022Review
- Predicting microbiomes through a deep latent space.Bioinformatics (Oxford, England) · 2021Article
- Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.Scientific reports · 2021Article
- Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Longitudinal studies are crucial for discovering causal relationships between the microbiome and human disease. We present MITRE, the Microbiome Interpretable Temporal Rule Engine, a supervised machine learning method for microbiome time-series analysis that infers human-interpretable rules linking changes in abundance of clades of microbes over time windows to binary descriptions of host status, such as the presence/absence of disease. We validate MITRE's performance on semi-synthetic data and five real datasets. MITRE performs on par or outperforms conventional difficult-to-interpret machine learning approaches, providing a powerful new tool enabling the discovery of biologically interpretable relationships between microbiome and human host ( https://github.com/gerberlab/mitre/ ).
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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.