Gosline works to develop computational algorithms that are uniquely targeted for rare disease work by doing foundational research in model system development. This work can be expanded to all model systems in human disease.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

PNNL is the newest member of the CONsortium on Nuclear sECurity Technologies (CONNECT).

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

Silicone wristbands could be more convenient option for collecting exposure data.

PNNL scientists have developed a new mass spectrometry method for in-depth analysis of proteins in individual cells.

Data from increasingly complex mass spectrometry instruments met with adaptable data analysis solution.

Researchers characterize protein signatures that indicate Acute Kidney Injury in COVID-19 patients.

Simulations reveal advantages of using new model vs. allometric scaling for risk assessment.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

Hypergraphs can more faithfully identify important genes in complex immune responses.

The web-based tool, called PLATIPUS, is publicly available and interacts with publications available in the CovidScholar database.

New quantum chemistry pipeline can help predict the effects of bioengineering.

High-throughput biochemical assays targeting a vital viral protein identified one molecule out of more than 13,000 with promising antiviral activity against SARS-CoV-2.

Understanding lipid composition of ant fungal gardens provides new knowledge on interkingdom communications band and also advances toward the development of microbial systems that can produce valuable compounds from plant biomass.

By studying discrete functional components of the soil microbiome at high resolution, researchers obtained a more complete picture of soil diversity compared to analysis of the entire soil community.

Researchers trained deep neural networks to accurately predict microbial interactions captured by fluorescence microscopy.

Researchers have developed a new technique to visualize the activity and distribution of enzymes that mobilize phosphate around plant roots.