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Updated: Jan 15, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
Published on: August 19, 2025
A Novel Machine Learning Post-processing Filter for Mass Spectrometry-Based Proteogenomics Leveraging Retention Time
Feifei Wei1,2, Tetsuro Sasada3,4
1Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan.
None:
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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