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

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...

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Detalles Bibliográficos
Autores principales: Bogart, Elijah, Creswell, Richard, Gerber, Georg K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721208/
https://www.ncbi.nlm.nih.gov/pubmed/31477162
http://dx.doi.org/10.1186/s13059-019-1788-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721208/
https://www.ncbi.nlm.nih.gov/pubmed/31477162
http://dx.doi.org/10.1186/s13059-019-1788-y

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