A major bottleneck in early detection is the molecular heterogeneity between ovarian cancer (OC) patients, which limits the likelihood of identifying individual biomarkers that are shared among patients. In a new study “A personalized probabilistic approach to ovarian cancer diagnostics,” published in Gynecologic Oncology, researchers from Georgia Tech have addressed this challenge by applying machine learning (ML) on patient metabolic profiles to identify biomarker patterns for personalized OC diagnosis. The Georgia Tech researchers include John McDonald, Professor Emeritus, School of Biological Sciences; Dongjo Ban, a Bioinformatics Ph.D. student in McDonald’s lab; Research Scientists Stephen N. Housley, Lilya V. Matyunina, and L.DeEtte (Walker) McDonald; and Regents’ Professor Jeffrey Skolnick, who also serves as Mary and Maisie Gibson Chair in the School of Biological Sciences and Georgia Research Alliance Eminent Scholar in Computational Systems Biology. (The study was also covered at The New York Post, Technology Networks, Medical Xpress, News-Medical.net, Medscape and Diagnostics World.)