Biosciences researchers have used Collaborative Cross mice, a mouse model system designed to mimic the diversity of human populations, to identify a set of genetic factors that could help refine treatment approaches for a fast-growing form of breast cancer.
Toward a Genetic Understanding of Variability in Radiation Sensitivity
Injury to immune-system and blood-forming cells is a common side effect of radiation therapy, which more than half of all cancer patients receive as part of their treatment. Biosciences Area researchers and their collaborators used a genetically diverse mouse population to model individual differences in sensitivity to radiation exposure.
Genetic Background Influences Cancer Risk of Thirdhand Smoke Exposure
A new study investigating the effect of thirdhand smoke (THS) in a mouse model system specially designed to mimic the genetic diversity of human populations has shed new light on how genetic predispositions contribute to an individual’s cancer risk. This work is an instrumental step towards building a more realistic understanding of how tobacco smoke residue could impact cancer risk in people.
Machine Learning Helps Link Chemical Exposure and Obesity
Scientists at Berkeley Lab and their collaborators developed a machine learning technique to discover obesity-related mixed chemical exposure patterns associated with environmental health risk in the general U.S. population. To assess this, they used indicators like body mass index and waist circumference.
Researchers at Berkeley Lab Advance Cancer Research Using Artificial Intelligence
BSE Researchers recently published two studies that will help oncologists more precisely understand the state of their patients’ disease or their risk for cancer relapse. As with many diseases, cancer can be challenging to predict and in some cases, impossible to treat. This work, however, is pushing the boundaries of how science and artificial intelligence (AI) can be used to better understand the risks and outcomes of cancer in human health.
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