The same algorithms that personalize movie recommendations and extract topics from oceans of text could bring doctors closer to diagnosing, treating and preventing disease on the basis of an individual’s unique genetic profile. In a study to be published Monday, Nov. 7 in Nature Genetics, researchers at Columbia and Princeton universities describe a new machine-learning algorithm for scanning massive genetic data sets to infer an individual’s ancestral makeup, which is key to identifying disease-carrying genetic mutations. “We’re excited to scale some of our recent machine learning tools to real-world problems in genetics,” said David Blei, a professor of computer science and statistics at Columbia University and member of the Data Science Institute.