Machine learning, a subfield of computer science, has been widely applied in many areas from science to engineering to many interdisciplinary fields. Nature Reviews Genetics recently published an article that summarized machine learning applications in genetics and genomics, authored by University of Washington researchers Maxwell W. Libbrecht and William Stafford Noble. The topics of interest included supervised versus unsupervised learning, generative versus discriminative modeling, incorporating prior knowledge, handling heterogeneous data, feature selection, imbalanced class sizes, handling missing data, and modelling dependence among examples. To read the full text of the review paper “Machine Learning Applications in Genetics and Genomics”, please visit http://www.nature.com/nrg/journal/v16/n6/full/nrg3920.html.