An interdisciplinary team of researchers has developed a machine learning framework that uses limited water quality samples to predict which inorganic pollutants are likely to be present in a groundwater supply. The new tool allows regulators and public health authorities to prioritize specific aquifers for water quality testing.

in one of the first studies of its kind, a group of researchers from the Keck School of Medicine of USC have developed a novel precision health framework that leverages multi-omics to understand environment-related diseases. The findings were published in the journal Environment International. To showcase how the proposed methods can be used towards precision health investigations, the team carried out a study examining how mercury exposure in utero might impact the risk of developing liver injury in childhood. Jesse Goodrich and his colleagues developed three data analysis approaches that integrated multi-omics to uncover insights on prenatal mercury induced childhood fatty liver disease, a common liver condition in children that can cause severe health issues.  The first method for analyzing this type of data identifies multi-omics biomarkers of environment-associated diseases years after the environmental exposure. The second method aims to identify altered biological pathways that inform environment-associated diseases. The third method identifies groups of individuals at the highest risk of disease based on different patterns of what they have been exposed to.