ArticleMethods in molecular biology (Clifton, N.J.)2026
A Novel Machine Learning Post-processing Filter for Mass Spectrometry-Based Proteogenomics Leveraging Retention Time Deviation and Peptide Physicochemical Properties.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The advancements in technology and computation are progressively enhancing the sensitivity and accuracy of mass spectrometry-based immunopeptidomics. In our previous study, we developed a machine learning filter by incorporating retention time as well as predicted physicochemical properties of peptides to eliminate false positives in identifications by Mascot-based traditional shotgun proteomic workflow. The present study provides a step-by-step guide on emphasizing how to prepare and organize the data, as well as applying the machine learning model to analyze the users' own experimental data.
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