Machine Learning Helps Clarify the Risk Connected to Age-Related Blood Condition

Artificial intelligence (AI) and machine learning allow researchers to study databases that otherwise would be too large and complex. In a recent study, Sloan Kettering Institute computational biologist Quaid Morris and collaborators used models to study an aging-related blood condition called clonal hematopoiesis (CH). Their research showed how evolution and natural selection influence CH and the effects that it may have on health outcomes. CH is relatively common in older people, affecting up to 10% of the population by age 80. The condition raises the risk of developing blood disorders -- including some blood cancers -- and cardiovascular disease. "One of the issues that we face in studying something complicated like CH is the interplay of many different factors," says Dr. Morris, who is co-senior author of a paper on CH published August 13, 2021, in Nature Communications.