Advanced proteomic analysis tools dig deep into human blood plasma to understand the molecular nature of advanced alcoholic disease.

Scientists have taken a key step toward harnessing a form of AI known as deep reinforcement learning to protect computer networks.

A detailed snapshot of proteins by PNNL scientists marks a big step toward a diagnostic blood test for a deadly form of liver disease.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

AAAS Fellows are a distinguished cadre of scientists, engineers, and innovators who have been recognized for their achievements across disciplines.

A paper by PNNL researchers has been chosen as one of 32 NIEHS Papers of the Year—a top 1% honor in the research field.

Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.

PNNL Biomedical Scientist Geremy Clair has taken on new roles as an editor for two journals; Frontiers In Cellular And Infection Microbiology and Frontiers In Molecular Biosciences.

Findings show method to better prepare human lung tissue for study; it represents the latest mass spectrometry-based omics advances.

PNNL mathematician Aaron Luttman contributed to the organizing committee for workshop exploring robust machine learning and artificial intelligence systems for the U.S. Army.

PNNL’s contributions over the last year span scientific discovery, sustainable energy and national security.

Clarivate recently released the list of Highly Cited Researchers for 2022.

The Joint Appointment program at PNNL is one of the most diverse among other U.S. national laboratories, involving nearly 60 universities and research institutions in the United States and abroad.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

Steven Spurgeon’s research is featured in the cover of the MRS Bulletin along with his team’s invited perspective on the future of machine learning for electron and scanning probe microscopy.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

New research findings published in Science Advances (November 2022), help explain the progression of Alzheimer-related dementia in each patient. The findings outline a biological classification system that predicts disease severity.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

Studying how microorganisms behave in space could facilitate farming in the final frontier and lead to better crops on Earth.