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• W3131629713 abstract "Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra—a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semisupervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study." @default.
• W3131629713 created "2021-03-01" @default.
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• W3131629713 date "2021-02-17" @default.
• W3131629713 modified "2023-10-17" @default.
• W3131629713 title "mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection" @default.
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• W3131629713 doi "https://doi.org/10.1021/acs.jproteome.0c01010" @default.
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