Notably, Percolator is extremely flexible, allowing its user to consider any set of features to describe a dataset of PSMs. Initially, features based on PSM metadata were used, such as the search engine score, the peptide length, and the charge state. However, recently several researchers have taken advantage of this flexibility by integrating features from newly developed ML-based predictors to boost the number of high-confidence identifications. The basic assumption is that correct peptides should closely resemble ground truth measurements, while features for incorrect peptides are more likely to follow a random distribution. Because it is infeasible to have ground truth measurements for all possible peptides and predictors have drastically improved in accuracy, predictions are used as a proxy for true ground truth data (Fig. ).