ArticlemSystems2022
MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics.
Article in mSystems, 2022. 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, 19 citations in OpenAlex.
- MCSPACE: inferring microbiome spatiotemporal dynamics from high-throughput co-localization data.Microbiome · 2025Article
- MMETHANE: interpretable AI for predicting host status from microbial composition and metabolomics data.Microbiome · 2025Article
- A systematic longitudinal study of microbiome: integrating temporal-spatial dimensions with causal and deep learning models.BMC genomics · 2025Article
- Review
- Optimization-based framework with flux balance analysis (FBA) and metabolic pathway analysis (MPA) for identifying metabolic objective functions.PLoS computational biology · 2025Article
- MIMIC: a Python package for simulating, inferring, and predicting microbial community interactions and dynamics.Bioinformatics (Oxford, England) · 2025Article
- Harnessing Gut Microbiota for Biomimetic Innovations in Health and Biotechnology.Biomimetics (Basel, Switzerland) · 2025Review
- 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
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Drug-microbiota interactions: an emerging priority for precision medicine.Signal transduction and targeted therapy · 2023Review
- Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.Current opinion in chemical biology · 2023Review
- Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. However, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, Microbiome Differentiable Interpretable Temporal Rule Engine (MDITRE), which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing data sets, we demonstrate that in almost all cases, MDITRE performs on par with or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through case studies can be used to derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes.
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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.