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Artificial intelligence is becoming an increasingly valuable tool for ORNL researchers tackling the many mysteries of cancer. Credit: Getty Images.

A team of researchers from ORNL was recognized by the National Cancer Institute in March for their unique contributions in the fight against cancer.

ORNL’s Adam Guss and colleagues used synthetic biology to develop a custom microbe capable of converting deconstructed mixed plastic waste into valuable new materials. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Scientists working on a solution for plastic waste have developed a two-step chemical and biological process to break down and upcycle mixed plastics into valuable bioproducts.

ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

A team of researchers has developed a novel, machine learning–based  technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.

A study led by researchers at ORNL could help make materials design as customizable as point-and-click.

ORNL scientists created a new microbial trait mapping process that improves on classical protoplast fusion techniques to identify the genes that trigger desirable genetic traits like improved biomass processing. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy. Reprinted with the permission of Oxford University Press, publisher of Nucleic Acids Research

ORNL scientists had a problem mapping the genomes of bacteria to better understand the origins of their physical traits and improve their function for bioenergy production.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography.  Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.

Carrie Eckert

Carrie Eckert applies her skills as a synthetic biologist at ORNL to turn microorganisms into tiny factories that produce a variety of valuable fuels, chemicals and materials for the growing bioeconomy.

ORNL metabolic engineer Adam Guss develops genetic tools to modify microbes that can perform a range of processes needed to create sustainable biofuels and bioproducts. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

As a metabolic engineer at Oak Ridge National Laboratory, Adam Guss modifies microbes to perform the diverse processes needed to make sustainable biofuels and bioproducts.

ORNL’s Josh Michener, a microbiologist and metabolic engineer, led the discovery of a useful new enzyme that breaks down stubborn bonds in lignin, a polymer found in plants that typically becomes waste during bioconversion. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

In a step toward increasing the cost-effectiveness of renewable biofuels and bioproducts, scientists at ORNL discovered a microbial enzyme that degrades tough-to-break bonds in lignin, a waste product of biorefineries.

An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus. Credit: Michelle Lehman/ORNL, U.S. Dept. of Energy

An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.