Proteins that guide transcription factors from the nuclear membrane to the DNA cause drug-resistant skin cancers and are new targets for drug development.
DNA looping, or folding, directs a cell's developmental fate. Harnessing this 'DNA origami' could help researchers generate specific tissues for therapies.
Geneticist Michael Snyder has tracked the expression of his genes for three years, focusing on changes in response to chronic or acute disease.
A Stanford team has developed an algorithm that uses data about tumors to identify new classifications that can provide information about patient outcomes
Stanford scientists identified two key genes responsible for the rapid bone growth of deer antlers, a finding that may one day help treat bone disease.
In a new study, a team of researchers has examined the relationship between protein binding to DNA and the development of cancer.
Stanford researchers have identified a small molecule that may help curb some of the symptoms of a genetic deficiency in glucose-6-phosphate dehydrogenase.
A new variation of gene-editing technology CRISPR allows scientists to reorganize DNA in a cell's nucleus in three dimensions, altering cell function.
Stanford scientists have found that viral infections shaped human genome evolution after interbreeding with Neanderthals 50,000 years ago.
Scientists find new potential drug targets for heart disease and diabetes, while shedding more light on the genetics of cholesterol, a new study has found.
The taller you are the more likely you are to get varicose veins, according to Stanford study that researched the genetics of half a million people.
Online outreach and low-cost testing can encourage relatives of cancer patients to assess their own cancer risk through 'cascade' testing.
A team of researchers has used an algorithm to improve newborn screening for genetic diseases, with the hopes of reducing the number of false positives.
The true driver mutations of cancer are almost always common to all metastases in an individual, according to a Stanford scientist and other researchers.
Scientists have developed an algorithm that combines genome sequence data and electronic health information to predict risk for genetic disease.
A workaround avoids a common, dangerous side effect of gene therapy: an autoimmune reaction to the normal protein, which could improve gene therapy.