Machine learning promises to accelerate metabolism research
A new study shows that it is possible to use machine learning and statistics to address a problem that has long hindered the field of metabolomics: large variations in the data collected at different sites.
“We don’t always know the source of the variation,” said Daniel Raftery, professor of anesthesiology and pain medicine at the University of Washington School of Medicine in Seattle. “It could be because the subjects are different with different genetics, diets and environmental exposures. Or it could be the way samples were collected and ...












