The Genesis Mission will mobilize the Department of Energy’s 17 National Laboratories, industry, and academia to build an integrated discovery platform.
Predicting how organisms’ characteristics respond to not only their genes, but also their environments (a nascent field called predictive phenomics), is extraordinarily challenging. Researchers at PNNL are using AI to tackle that challenge.
Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.
Continued studies will deepen scientists’ understanding of virus-host interactions at the molecular level and also pave the way for developing better drugs to fight emerging viruses.
Lauren Charles, a chief data scientist at PNNL, showcased the vital research coming out of her program at The National Academies Forum workshop in Washington, D.C., January 15–16, 2025.
Over the next four years, PNNL and University of Arizona will develop open-source computational tools to better identify and characterize the viruses associated with the human microbiome.
Armed with some of the world’s most advanced instrumentation, researchers at PNNL are working to analyze huge amounts of data and uncover hidden biological connections.
The Pacific Northwest Association of Toxicologists (PANWAT) presented its annual Toxicology Achievement Award to Katrina Waters at the Society of Toxicology Pacific Northwest Chapter Meeting, held in Lynnwood, Washington, on September 30th.
Neeraj Kumar discusses how AI can transform scientific research at the Platform for Advanced Scientific Computing Conference and Trillion Parameter Consortium European Workshop.
Early life exposure to polycyclic aromatic hydrocarbons (PAHs), found in smoke, has been linked to developmental problems. To study the impacts of these pollutants, PAH metabolism in infants and adults were compared.