MITRE: inferring features from microbiota time-series data linked to host status.

Clicks: 192
ID: 33223
2019
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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/ ).
Reference Key
bogart2019mitregenome Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bogart, Elijah;Creswell, Richard;Gerber, Georg K;
Journal Genome biology
Year 2019
DOI
10.1186/s13059-019-1788-y
URL
Keywords Keywords not found

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