Using Machine Learning to Battle Antibiotic Resistance

The team applied a machine learning algorithm called extreme gradient boosting (XGBoost). Using those 10-mer counts, the computer designs decision trees to predict the right MICs. Each decision point uses one of the 10-mers to help it classify a given genome as resistant or susceptible to various drugs. The algorithm then assigns different levels of importance to each 10-mer, and designs trees repeatedly, in rounds called "boosts," until it gets the lowest error it can for its MIC predictions compared to the true MICs. The researchers ran the algorithm 10 times, each time leaving out a different tenth of their dataset.