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Journal Abstract Search

198 related items for PubMed ID: 23095859

  • 1. MUMAL: multivariate analysis in shotgun proteomics using machine learning techniques.
    Cerqueira FR, Ferreira RS, Oliveira AP, Gomes AP, Ramos HJ, Graber A, Baumgartner C.
    BMC Genomics; 2012; 13 Suppl 5(Suppl 5):S4. PubMed ID: 23095859
    [Abstract] [Full Text] [Related]

  • 2. MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.
    Cerqueira FR, Ricardo AM, de Paiva Oliveira A, Graber A, Baumgartner C.
    BMC Bioinformatics; 2016 Dec 15; 17(Suppl 18):472. PubMed ID: 28105913
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  • 3. MUDE: a new approach for optimizing sensitivity in the target-decoy search strategy for large-scale peptide/protein identification.
    Cerqueira FR, Graber A, Schwikowski B, Baumgartner C.
    J Proteome Res; 2010 May 07; 9(5):2265-77. PubMed ID: 20199108
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  • 4. Decoy methods for assessing false positives and false discovery rates in shotgun proteomics.
    Wang G, Wu WW, Zhang Z, Masilamani S, Shen RF.
    Anal Chem; 2009 Jan 01; 81(1):146-59. PubMed ID: 19061407
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  • 5. Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets.
    Spivak M, Weston J, Bottou L, Käll L, Noble WS.
    J Proteome Res; 2009 Jul 01; 8(7):3737-45. PubMed ID: 19385687
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  • 8. A peptide-retrieval strategy enables significant improvement of quantitative performance without compromising confidence of identification.
    Tu C, Shen S, Sheng Q, Shyr Y, Qu J.
    J Proteomics; 2017 Jan 30; 152():276-282. PubMed ID: 27903464
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  • 11. Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics.
    Madej D, Wu L, Lam H.
    J Proteome Res; 2022 Feb 04; 21(2):339-348. PubMed ID: 34989576
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  • 12. Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.
    Pfeifer N, Leinenbach A, Huber CG, Kohlbacher O.
    BMC Bioinformatics; 2007 Nov 30; 8():468. PubMed ID: 18053132
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  • 13. APP: an Automated Proteomics Pipeline for the analysis of mass spectrometry data based on multiple open access tools.
    Malm EK, Srivastava V, Sundqvist G, Bulone V.
    BMC Bioinformatics; 2014 Dec 30; 15(1):441. PubMed ID: 25547515
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  • 14. JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy.
    Wang X, Li Y, Wu Z, Wang H, Tan H, Peng J.
    Mol Cell Proteomics; 2014 Dec 30; 13(12):3663-73. PubMed ID: 25202125
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  • 15. Semi-supervised learning for peptide identification from shotgun proteomics datasets.
    Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ.
    Nat Methods; 2007 Nov 30; 4(11):923-5. PubMed ID: 17952086
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  • 16. Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics.
    Keich U, Kertesz-Farkas A, Noble WS.
    J Proteome Res; 2015 Aug 07; 14(8):3148-61. PubMed ID: 26152888
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  • 19. Enhanced peptide identification by electron transfer dissociation using an improved Mascot Percolator.
    Wright JC, Collins MO, Yu L, Käll L, Brosch M, Choudhary JS.
    Mol Cell Proteomics; 2012 Aug 07; 11(8):478-91. PubMed ID: 22493177
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