Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

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.

PNNL researchers have published their paper, “Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data,” in the Journal of Proteome Research.

A new study led by PNNL researcher TC Chakraborty provides some answers about how irrigated agricultural land near cities affects urban heat stress.

CACTUS provides AI-powered cheminformatics for autonomous science.

PNNL scientists use proteomics to unveil how high-density lipoproteins protect against cholesterol accumulation in the artery wall.

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.

PMedIC, the PNNL and OHSU joint research collaboration, contributes to student success and impact in biomedicine.

Discovering and measuring the spatial organization of proteins within cells allows scientists to map complex proteoforms across tissues with near-cellular resolution.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

Data reveals mechanisms in our bodies that can help us fight viral pathogenesis.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

COVID-19 infections at PNNL early in the pandemic were caused by a wide variety of viral sequences, according to a new analysis by Laboratory researchers.