University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > A Bayesian multi-task regression model for detecting global microbiome associations

A Bayesian multi-task regression model for detecting global microbiome associations

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Host: Dr Shan He (s.he@cs.bham.ac.uk)

Abstract: The human gut microbiome has been shown to be associated with a variety of human diseases, including cancer, metabolic conditions and inflammatory bowel disease. Current statistical techniques for microbiome association studies often rely on a measure of ecological distance, or on detecting associations with individual bacterial species. In this work, we develop a novel Bayesian multi-task approach for detecting global microbiome associations. Our method is not dependent on a choice of distance measure, and is able to incorporate phylogenetic information about microbial species. We apply our method to simulated data and show that it allows for consistent estimation of global microbiome effects. Additionally, we investigate the performance of the model on a real-world microbiome study in inflammatory bowel disease (Beamish et al., 2017) and show that we can use it to detect microbiome-metabolome associations.

Website: http://frankdondelinger.org/

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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