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    Nat Methods. 2007 Nov;4(11):923-5. Epub 2007 Oct 21.

    Semi-supervised learning for peptide identification from shotgun proteomics datasets.

    Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ.

    Department of Genome Sciences, University of Washington, 1705 NE Pacific St., William H. Foege Building, Seattle, Washington 98195, USA.

    Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic Saccharomyces cerevisiae dataset, and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.

    PMID: 17952086 [PubMed - indexed for MEDLINE]

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