Cancer Diagnostics – News and Features

Variants of the Ras gene in thyroid tumors could be used for more accurate cancer diagnoses and treatment.

Researchers have used AI to unearth potential cancer drivers hidden in so-called "junk" regions of DNA, opening the door for a new approach to diagnosis and treatment.

Researchers have developed an AI model that increases the potential for detecting cancer through sugar analyses.

UZH and USZ researchers have now further developed an advanced method to analyze liquid biopsies of DNA fragments in the blood. It is fast and practical, without putting much of a strain on patients.

Scientists have developed a new AI architecture designed to detect breast cancer that the researchers say has achieved an accuracy rate of 99.72%.

An artificial intelligence-powered method for detecting tumor DNA in blood has shown unprecedented sensitivity in predicting cancer recurrence, in a study led by researchers at Weill Cornell Medicine.

Scientists have developed a computational method that links genes to the molecular marks on DNA that "switch" them on or off.

Both genetic and epigenetic sample information is required to get the full picture. Six-base genome sequencing enables this without need of multiple techniques, laborious downstream data processing or data loss, and cancer research is benefiting.

An experimental treatment made from a plant virus is effective at protecting against a broad range of metastatic cancers in mice, shows a new study from the University of California San Diego.

How can you identify which technique is best suited to your research requirements? This article explores the advantages of qPCR and dPCR, considering specific applications and deliverable metrics to help you choose the right technology.