Machine learning identifies the likely “initiator” and enables new predictions about DNA mutations that can cause disease.

Every human cell depends on tens of thousands of genes being switched on at the right time and in the right amount. Specialized stretches of DNA coordinate this activity, ultimately directing the production of enzymes, hormones, proteins, and other components essential to cell structure and function. When that regulation goes wrong, cells can malfunction and contribute to disorders including cancer.

To better understand the DNA sequences that control this process, researchers in the University of California San Diego Professor James T. Kadonaga’s laboratory focused on a crucial region known as the “initiator.” This DNA segment marks the point where information encoded in a gene first begins to be converted, or expressed, into functional products.