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Semi-supervised learning for peptide identification from shotgun proteomics datasets

Semi-supervised learning for peptide identification from shotgun proteomics datasets,10.1038/nmeth1113,Nature Methods,Lukas Käll,Jesse D Canterbury,Ja

Semi-supervised learning for peptide identification from shotgun proteomics datasets   (Citations: 26)
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Journal: Nature Methods - NAT METHODS , vol. 4, no. 11, pp. 923-925, 2007
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    • ...van den Toorn et al, 2011) to filter all peptides (modified and unmodified) to a 1% peptide FDR, using the Mascot Percolator option...

    Vera van Noortet al. Cross-talk between phosphorylation and lysine acetylation in a genome-...

    • ...Results from these searches were analyzed with the PERCOLA-TOR program [65,66] to improve discrimination between correct and incorrect peptide-spectrum matches and to set a per-spectrum false discovery rate (FDR) of 0.01...

    Matthew D Deanet al. Identification of ejaculated proteins in the house mouse ( Mus domesti...

    • ... This is analogous to the situation in early shotgun proteomics, in which the tools to control the quality of the identification were developed long after the principal measurement method...

    Lukas Reiteret al. mProphet: automated data processing and statistical validation for lar...

    • ...proteomics leaders can step into this trap as illustrated by Percolator [4] that originally was published as a non-TDAcompliant tool (shortly after publishing [4], the authors of Percolator realized that it was not TDA-compliant and corrected it in [5], see Supplement J)...
    • ...proteomics leaders can step into this trap as illustrated by Percolator [4] that originally was published as a non-TDAcompliant tool (shortly after publishing [4], the authors of Percolator realized that it was not TDA-compliant and corrected it in [5], see Supplement J)...
    • ...This result underscores the danger of using database-dependent scoring functions and raises the concern of whether some MS/MS tools feature respectable FDRs while generating some low-quality PSMs (see Supplement C). This question is far from being theoretical since the original version of Percolator and some other tools indeed fell into this trap [4, 5]...

    Nitin Guptaet al. Target-Decoy Approach and False Discovery Rate: When Things May Go Wro...

    • ...Machine learning techniques are widely used to build re-ranking models [9-14]...
    • ...One may argue that some semi-supervised learning methods such as Percolator [10] do not require any training data...

    Zengyou Heet al. Score regularization for peptide identification

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