Abstract
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 processed.” Raftery and his research colleagues wanted to see if machine learning - a form of artificial intelligence that uses computer algorithms to process large volumes of historical data and to identify data patterns - could reduce this variation between data from different sites without obscuring important differences.
NETL
NSTL