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Items: 1 to 20 of 97

1.

A non-parametric cutout index for robust evaluation of identified proteins.

Serang O, Paulo J, Steen H, Steen JA.

Mol Cell Proteomics. 2013 Mar;12(3):807-12. doi: 10.1074/mcp.O112.022863. Epub 2013 Jan 4.

2.

Properties of average score distributions of SEQUEST: the probability ratio method.

Martínez-Bartolomé S, Navarro P, Martín-Maroto F, López-Ferrer D, Ramos-Fernández A, Villar M, García-Ruiz JP, Vázquez J.

Mol Cell Proteomics. 2008 Jun;7(6):1135-45. doi: 10.1074/mcp.M700239-MCP200. Epub 2008 Feb 25.

3.

Bayesian nonparametric model for the validation of peptide identification in shotgun proteomics.

Zhang J, Ma J, Dou L, Wu S, Qian X, Xie H, Zhu Y, He F.

Mol Cell Proteomics. 2009 Mar;8(3):547-57. doi: 10.1074/mcp.M700558-MCP200. Epub 2008 Nov 12.

4.

An automated proteomic data analysis workflow for mass spectrometry.

Pendarvis K, Kumar R, Burgess SC, Nanduri B.

BMC Bioinformatics. 2009 Oct 8;10 Suppl 11:S17. doi: 10.1186/1471-2105-10-S11-S17.

5.

Empirical approach to false discovery rate estimation in shotgun proteomics.

Goloborodko AA, Mayerhofer C, Zubarev AR, Tarasova IA, Gorshkov AV, Zubarev RA, Gorshkov MV.

Rapid Commun Mass Spectrom. 2010 Feb;24(4):454-62. doi: 10.1002/rcm.4417.

PMID:
20069687
6.

Empirical Bayes analysis of quantitative proteomics experiments.

Margolin AA, Ong SE, Schenone M, Gould R, Schreiber SL, Carr SA, Golub TR.

PLoS One. 2009 Oct 14;4(10):e7454. doi: 10.1371/journal.pone.0007454.

7.

Improving mass and liquid chromatography based identification of proteins using bayesian scoring.

Chen SS, Deutsch EW, Yi EC, Li XJ, Goodlett DR, Aebersold R.

J Proteome Res. 2005 Nov-Dec;4(6):2174-84.

PMID:
16335964
8.

Effectiveness of CID, HCD, and ETD with FT MS/MS for degradomic-peptidomic analysis: comparison of peptide identification methods.

Shen Y, Tolić N, Xie F, Zhao R, Purvine SO, Schepmoes AA, Moore RJ, Anderson GA, Smith RD.

J Proteome Res. 2011 Sep 2;10(9):3929-43. doi: 10.1021/pr200052c. Epub 2011 Aug 15.

10.

Empirical Bayesian random censoring threshold model improves detection of differentially abundant proteins.

Koopmans F, Cornelisse LN, Heskes T, Dijkstra TM.

J Proteome Res. 2014 Sep 5;13(9):3871-80. doi: 10.1021/pr500171u. Epub 2014 Aug 22.

PMID:
25102230
11.

Assigning spectrum-specific P-values to protein identifications by mass spectrometry.

Spirin V, Shpunt A, Seebacher J, Gentzel M, Shevchenko A, Gygi S, Sunyaev S.

Bioinformatics. 2011 Apr 15;27(8):1128-34. doi: 10.1093/bioinformatics/btr089. Epub 2011 Feb 23.

12.
13.

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines.

Jones AR, Siepen JA, Hubbard SJ, Paton NW.

Proteomics. 2009 Mar;9(5):1220-9. doi: 10.1002/pmic.200800473.

14.

A combinatorial perspective of the protein inference problem.

Yang C, He Z, Yu W.

IEEE/ACM Trans Comput Biol Bioinform. 2013 Nov-Dec;10(6):1542-7. doi: 10.1109/TCBB.2013.110.

PMID:
24407311
15.

Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry.

Käll L, Storey JD, Noble WS.

Bioinformatics. 2008 Aug 15;24(16):i42-8. doi: 10.1093/bioinformatics/btn294.

16.

A statistical model for identifying proteins by tandem mass spectrometry.

Nesvizhskii AI, Keller A, Kolker E, Aebersold R.

Anal Chem. 2003 Sep 1;75(17):4646-58.

PMID:
14632076
17.

A Bayesian approach to peptide identification using accurate mass and time tags from LC-FTICR-MS proteomics experiments.

Yanofsky CM, Kearney RE, Lesimple S, Bergeron JJ, Boismenu D, Carrillo B, Bell AW.

Conf Proc IEEE Eng Med Biol Soc. 2008;2008:3775-8. doi: 10.1109/IEMBS.2008.4650030.

PMID:
19163533
18.

Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics.

Choi H, Nesvizhskii AI.

J Proteome Res. 2008 Jan;7(1):254-65. Epub 2007 Dec 27.

PMID:
18159924
19.

Tandem Mass Spectrum Identification via Cascaded Search.

Kertesz-Farkas A, Keich U, Noble WS.

J Proteome Res. 2015 Aug 7;14(8):3027-38. doi: 10.1021/pr501173s. Epub 2015 Jun 30.

20.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.

Nesvizhskii AI.

J Proteomics. 2010 Oct 10;73(11):2092-123. doi: 10.1016/j.jprot.2010.08.009. Epub 2010 Sep 8. Review.

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