Repeats of DNA sequences, often referred to as “junk DNA” or “dark matter,” that are found in chromosomes and could contribute to cancer or other diseases have been challenging to identify and characterize. Now, investigators at the Johns Hopkins Kimmel Cancer Center have developed a novel approach that uses machine learning to identify these elements in cancerous tissue, as well as in cell-free DNA (cfDNA) — fragments that are shed from tumors and float in the bloodstream. This new method could provide a noninvasive means of detecting cancers or monitoring response to therapy. Machine learning is a type of artificial intelligence that uses data and computer algorithms to perform complex tasks and accelerate research.