"Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models" by Darren Flower of the Jenner Institute and Tongbin Li of the University of Minnesota qualified for Hot Paper status based on its citation rate in the 20 months since its publication. The paper describes the use of machine
learning techniques to predict the binding of peptides to major histocompatibility
complex proteins

As Dr Li told Essential
Science Indicators, "Improved models of peptide-MHC interactions will lead to savings in cost and experimental effort in immunology research, and, in the long run, will improve people’s health".