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/).
CITATION STYLE
Bogart, E., Creswell, R., & Gerber, G. K. (2019). MITRE: Inferring features from microbiota time-series data linked to host status. Genome Biology, 20(1). https://doi.org/10.1186/s13059-019-1788-y
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