Random forest and gradient boosting algorithms, including XGBoost, have become dependable tools for analyzing biological data because they tolerate messy, mixed-type inputs without extensive ...

Machine learning (ML) now shows up somewhere in nearly every life science data analysis pipeline, from a variant caller flagging a candidate mutation to a clustering algorithm sorting cell types in a ...