Machine-learning models predict human health from microbiome data
Inside the gut, on the surface of the skin and lining your ears, mouth and nose are colonies of millions of microbes. For every human cell you have, there are at least as many microbial cells associated with your body.
The human microbiome, containing trillions of microbes, plays a crucial role in health. Lawrence Livermore National Laboratory researchers are investigating how microbiome data can predict, diagnose, and even prevent diseases in humans. To leverage the microbiome as a diagnostic tool, scientists are employing machine-learning algorithms on vast amounts of sequencing data from numerous studies.
However, compiling and curating this data presents challenges due to its sparsity and uncertainty in mapping DNA sequences to specific organisms. To address these issues, the team developed two main machine-learning approaches. The first, a hierarchical Bayesian model, accounts for uncertainty in the data and can identify disease-associated microbes, providing interpretable results.
The second approach uses computer vision models to convert microbiome data into images, where each pixel represents a microbe and its brightness indicates abundance. These visual patterns can then be analyzed by computer vision programs to learn patterns associated with different diseases. The researchers are actively exploring how various factors, such as lifestyle, diet, and geographical location, influence microbiome patterns and disease susceptibility.
Their work aims to predict disease risk, resilience, and treatment outcomes in military personnel and civilians.
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