Can Machine Learning Find Meaning in a Mess of Genes?

"We don't have much ground truth in biology." According to Barbara Engelhardt, a computer scientist at Princeton University, that's just one of the many challenges that researchers face when trying to prime traditional machine-learning methods to analyze genomic data. Techniques in artificial intelligence and machine learning are dramatically altering the landscape of biological research, but Engelhardt doesn't think those "black box" approaches are enough to provide the insights necessary for understanding, diagnosing and treating disease. Instead, she's been developing new statistical tools that search for expected biological patterns to map out the genome's real but elusive "ground truth." Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.