Scientists at the UC San Francisco (UCSF) Quantitative Biosciences Institute (QBI) and the QBI Coronavirus Research Group (QCRG) have been awarded $67.5 million from the National Institute of Allergy and Infectious Diseases (NIAID) to support its mission of pandemic preparedness.
Researchers at Children’s Hospital of Philadelphia (CHOP) have developed a new tool to help researchers interpret the clinical significance of somatic mutations in cancer. The tool, known as CancerVar, incorporates machine learning frameworks to go beyond merely identifying somatic cancer mutations and interpret the potential significance of those mutations in terms of cancer diagnosis, prognosis, and targetability. A paper describing CancerVar was published today in Science Advances.
Dana Pe’er, PhD, computational biologist and lab head at Memorial Sloan Kettering Cancer Center’s (MSK) Sloan Kettering Institute (SKI), is one of 33 biomedical researchers named a Howard Hughes Medical Institute (HHMI) investigator today.
Argonne, industry and academia collaborate to bring innovative AI and simulation tools to the COVID-19 battlefront.
UC San Diego School of Medicine researchers discovered gene expression patterns associated with pandemic viral infections, providing a map to help define patients’ immune responses, measure disease severity, predict outcomes and test therapies — for current and future pandemics.
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.
In advance of Argonne’s Aurora exascale supercomputer, Duke University assistant professor Amanda Randles is leading a new study to analyze cancer metastasis using HARVEY, a code that simulates blood vessels within the human body.
March Science Snapshots from Berkeley Lab
As large multi-cancer datasets become more important for predicting who may benefit from cancer drugs, a new model better accounts for potentially overlooked variation.
Ten organizations have created a pipeline of artificial intelligence and simulation tools to narrow the search for drug candidates that can inhibit SARS-CoV-2.
The research team will focus on 12 understudied plant groups — including Kadua, Wikstroemia and Psychotria — using new sequencing and modeling techniques to gain a broader idea of how Hawaiian plant diversity originated. The project includes conservation and educational components as well, including collecting new wild specimens for the National Tropical Botanical Garden herbarium.
Argonne scientists and research facilities have made a difference in the fight against COVID-19 in the year since the first gene sequence for the virus was published.
Using a combination of AI and supercomputing resources, Argonne researchers are examining the dynamics of the SARS-CoV-2 spike protein to determine how it fuses with the human host cell, advancing the search for drug treatments.
Reported in Nature Biotechnology, the known diversity of bacteria and archaea has been expanded by 44% through a publicly available collection of more than 52,000 microbial genomes from environmental samples resulting from a JGI-led collaboration involving more than 200 scientists around the world.
A team led by the Department of Energy’s Oak Ridge National Laboratory created a computational model of the proteins responsible for the transformation of mercury to toxic methylmercury, marking a step forward in understanding how the reaction occurs and how mercury cycles through the environment.
Researchers at Children’s Hospital of Philadelphia (CHOP) have developed a new computational algorithm that has, for the first time, identified a spectrum of mutations in the noncoding portion of the human genome across five major pediatric cancers. The study, which was published today in Science Advances, used the algorithm to analyze more than 500 pediatric cancer patients’ mutations and gene expression profiles to develop a comprehensive list of potentially cancer-causing mutations.
The U.S. Department of Energy (DOE) announced $5 million in funding for six new research projects in computational biology.
Watching and measuring what happens in tissues inside the human embryo is currently not possible, and it’s difficult to do in mammalian models. Because humans and the fruit fly Drosophila share so many biological similarities, Columbia Engineering and Syracuse University researchers tackled this problem by focusing on fruit flies. The team reports today that they can predict when the tissue will begin to rapidly flow just by looking at cell shapes in the tissue.
Recent discoveries by two research teams in the Ira A. Fulton Schools of Engineering at Arizona State University are advancing the field of synthetic biology. Results from a research collaboration between the lab groups of Assistant Professor Xiaojun Tian and Associate Professor have revealed novel ways that engineered gene circuits interact with biological host cells.
Argonne scientists are working around the clock to analyze the virus to find new treatments and cures, predict how it will propagate through the population, and make sure that our supply chains remain intact.
NIGMS grantee and presidential award recipient Sohini Ramachandran, Ph.D., is challenging our understanding of genetic variation among human populations. She discusses her research on how the genetic composition of traits and diseases varies among populations, the value of statistical and computational work in human genetics, and what this all means for patient treatment.
Sepsis causes nearly 270,000 deaths in the United States each year. Find out how big data approaches are helping clinicians catch it sooner, treat it better, and help survivors cope with long-term effects.
A multi-institutional group of researchers led by Children’s Hospital of Philadelphia (CHOP) has linked a strong cancer driver gene to changes in proteins that regulate alternative splicing. The researchers created new computational tools and biological model systems for the study. This collaborative research, led by Yi Xing, PhD, at CHOP and Owen Witte, MD, at the University of California, Los Angeles (UCLA), was published today in the Proceedings of the National Academy of Sciences.