165 related articles for article (PubMed ID: 34989576)
41. Accurate Identification of Deamidation and Citrullination from Global Shotgun Proteomics Data Using a Dual-Search Delta Score Strategy.
Wang X; Swensen AC; Zhang T; Piehowski PD; Gaffrey MJ; Monroe ME; Zhu Y; Dong H; Qian WJ
J Proteome Res; 2020 Apr; 19(4):1863-1872. PubMed ID: 32175737
[TBL] [Abstract][Full Text] [Related]
42. Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups.
The M; Samaras P; Kuster B; Wilhelm M
Mol Cell Proteomics; 2022 Dec; 21(12):100437. PubMed ID: 36328188
[TBL] [Abstract][Full Text] [Related]
43. Empirical multidimensional space for scoring peptide spectrum matches in shotgun proteomics.
Ivanov MV; Levitsky LI; Lobas AA; Panic T; Laskay ÜA; Mitulovic G; Schmid R; Pridatchenko ML; Tsybin YO; Gorshkov MV
J Proteome Res; 2014 Apr; 13(4):1911-20. PubMed ID: 24571493
[TBL] [Abstract][Full Text] [Related]
44. Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies.
Blakeley P; Overton IM; Hubbard SJ
J Proteome Res; 2012 Nov; 11(11):5221-34. PubMed ID: 23025403
[TBL] [Abstract][Full Text] [Related]
45. A decoy-free approach to the identification of peptides.
Gonnelli G; Stock M; Verwaeren J; Maddelein D; De Baets B; Martens L; Degroeve S
J Proteome Res; 2015 Apr; 14(4):1792-8. PubMed ID: 25714903
[TBL] [Abstract][Full Text] [Related]
46. False discovery rates and related statistical concepts in mass spectrometry-based proteomics.
Choi H; Nesvizhskii AI
J Proteome Res; 2008 Jan; 7(1):47-50. PubMed ID: 18067251
[TBL] [Abstract][Full Text] [Related]
47. A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets.
Savitski MM; Wilhelm M; Hahne H; Kuster B; Bantscheff M
Mol Cell Proteomics; 2015 Sep; 14(9):2394-404. PubMed ID: 25987413
[TBL] [Abstract][Full Text] [Related]
48. Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process.
Feng XD; Li LW; Zhang JH; Zhu YP; Chang C; Shu KX; Ma J
BMC Genomics; 2017 Mar; 18(Suppl 2):143. PubMed ID: 28361671
[TBL] [Abstract][Full Text] [Related]
49. Decoy-free protein-level false discovery rate estimation.
Teng B; Huang T; He Z
Bioinformatics; 2014 Mar; 30(5):675-81. PubMed ID: 23926225
[TBL] [Abstract][Full Text] [Related]
50. New glycoproteomics software, GlycoPep Evaluator, generates decoy glycopeptides de novo and enables accurate false discovery rate analysis for small data sets.
Zhu Z; Su X; Go EP; Desaire H
Anal Chem; 2014 Sep; 86(18):9212-9. PubMed ID: 25137014
[TBL] [Abstract][Full Text] [Related]
51. A cost-sensitive online learning method for peptide identification.
Liang X; Xia Z; Jian L; Wang Y; Niu X; Link AJ
BMC Genomics; 2020 Apr; 21(1):324. PubMed ID: 32334531
[TBL] [Abstract][Full Text] [Related]
52. Crescendo: A Protein Sequence Database Search Engine for Tandem Mass Spectra.
Wang J; Zhang Y; Yu Y
J Am Soc Mass Spectrom; 2015 Jul; 26(7):1077-84. PubMed ID: 25895889
[TBL] [Abstract][Full Text] [Related]
53. 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. PubMed ID: 19253293
[TBL] [Abstract][Full Text] [Related]
54. Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics.
Prieto G; Vázquez J
J Proteome Res; 2020 Mar; 19(3):1285-1297. PubMed ID: 32037837
[TBL] [Abstract][Full Text] [Related]
55. AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics.
Li Y; He Q; Guo H; Shuai SC; Cheng J; Liu L; Shuai J
J Proteome Res; 2024 Feb; 23(2):834-843. PubMed ID: 38252705
[TBL] [Abstract][Full Text] [Related]
56. Large Scale Mass Spectrometry-based Identifications of Enzyme-mediated Protein Methylation Are Subject to High False Discovery Rates.
Hart-Smith G; Yagoub D; Tay AP; Pickford R; Wilkins MR
Mol Cell Proteomics; 2016 Mar; 15(3):989-1006. PubMed ID: 26699799
[TBL] [Abstract][Full Text] [Related]
57. PTMiner: Localization and Quality Control of Protein Modifications Detected in an Open Search and Its Application to Comprehensive Post-translational Modification Characterization in Human Proteome.
An Z; Zhai L; Ying W; Qian X; Gong F; Tan M; Fu Y
Mol Cell Proteomics; 2019 Feb; 18(2):391-405. PubMed ID: 30420486
[TBL] [Abstract][Full Text] [Related]
58. 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; 152():276-282. PubMed ID: 27903464
[TBL] [Abstract][Full Text] [Related]
59. ProteoStats--a library for estimating false discovery rates in proteomics pipelines.
Yadav AK; Kadimi PK; Kumar D; Dash D
Bioinformatics; 2013 Nov; 29(21):2799-800. PubMed ID: 23962616
[TBL] [Abstract][Full Text] [Related]
60. An easy-to-use Decoy Database Builder software tool, implementing different decoy strategies for false discovery rate calculation in automated MS/MS protein identifications.
Reidegeld KA; Eisenacher M; Kohl M; Chamrad D; Körting G; Blüggel M; Meyer HE; Stephan C
Proteomics; 2008 Mar; 8(6):1129-37. PubMed ID: 18338823
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
