Rescoring: Leveraging Machine Learning for Enhanced Immunopeptidomics Data Analysis To address challenges in MS-based immunopeptidomics data analysis, PSM rescoring has emerged as a machine learning-based solution that can significantly reduce false positive spectrum assignments and improve immunopeptide identification rates. During PSM rescoring, machine learning algorithms such as Percolator , PeptideProphet , and mokapot , are used to post-process unfiltered database search results and learn an optimized score that effectively distinguishes between correct and incorrect PSMs based on various informative PSM features. PSM rescoring is most often done using Percolator , which uses semi-supervised machine learning (ML) to build a linear support vector machine (SVM)-based classifier that can distinguish between correct and incorrect PSMs. It begins by parsing the initial unfiltered database search results into two groups: PSMs with high scores from target peptides (positives) and PSMs from decoy peptides (negatives). Then (2) an SVM-based classifier is trained to discriminate between the positives and the negatives, based on features describing the PSMs. Finally, (3) a new score is calculated for all PSMs using the trained classifier. These three steps are repeated several times until convergence is reached. The newly computed scores should result in a greater number of confidently identified peptides, improving the peptide identification performance (Fig. ).