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

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/ ).

SUBMITTER: Bogart E

PROVIDER: S-EPMC6721208 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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

Bogart Elijah E Creswell Richard R Gerber Georg K GK

Genome biology 20190902 1

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 ...[more]

PMID: 31477162

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