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Semi-supervised lea...
Semi-supervised learning for peptide identification from shotgun proteomics datasets
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- Käll, Lukas (författare)
- Department of Genome Sciences, University of Washington
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Canterbury, Jesse D. (författare)
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Weston, Jason (författare)
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Noble, William Stafford (författare)
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MacCoss, Michael J. (författare)
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(creator_code:org_t)
- 2007-10-21
- 2007
- Engelska.
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Ingår i: Nature Methods. - : Springer Science and Business Media LLC. - 1548-7091 .- 1548-7105. ; 4:11, s. 923-925
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Publikations- och innehållstyp
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- art (ämneskategori)
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