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

Genome Biology

Georg K. Gerber¹  Richard Creswell¹  Elijah Bogart¹ 

[1] Massachusetts Host-Microbiome Center, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School;

关键词: Microbiome;    Time series;    Longitudinal;    Machine learning;   

DOI  :  10.1186/s13059-019-1788-y

来源: DOAJ

【 摘 要 】

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/).

【 授权许可】

Unknown