A machine-learning approach clarifies interactions between contaminants of emerging concern
Humans and biotas are exposed to a cocktail of contaminants of emerging concern (CECs), but mixture regulation is lagging behind. This is largely attributed to inadequate experimental data of mixture risk; revealing intricate interactions among CECs in mixtures with random combinations remains a formidable challenge. Here, we propose a new framework comprised of 5,720 lab tests of mixture risk for
