191 related articles for article (PubMed ID: 26193490)
1. Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics.
Langley SR; Mayr M
J Proteomics; 2015 Nov; 129():83-92. PubMed ID: 26193490
[TBL] [Abstract][Full Text] [Related]
2. A multi-model statistical approach for proteomic spectral count quantitation.
Branson OE; Freitas MA
J Proteomics; 2016 Jul; 144():23-32. PubMed ID: 27260494
[TBL] [Abstract][Full Text] [Related]
3. QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.
Choi H; Kim S; Fermin D; Tsou CC; Nesvizhskii AI
J Proteomics; 2015 Nov; 129():121-126. PubMed ID: 26254008
[TBL] [Abstract][Full Text] [Related]
4. mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.
Teo G; Kim S; Tsou CC; Collins B; Gingras AC; Nesvizhskii AI; Choi H
J Proteomics; 2015 Nov; 129():108-120. PubMed ID: 26381204
[TBL] [Abstract][Full Text] [Related]
5. Detecting differential and correlated protein expression in label-free shotgun proteomics.
Zhang B; VerBerkmoes NC; Langston MA; Uberbacher E; Hettich RL; Samatova NF
J Proteome Res; 2006 Nov; 5(11):2909-18. PubMed ID: 17081042
[TBL] [Abstract][Full Text] [Related]
6. Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.
Ramus C; Hovasse A; Marcellin M; Hesse AM; Mouton-Barbosa E; Bouyssié D; Vaca S; Carapito C; Chaoui K; Bruley C; Garin J; Cianférani S; Ferro M; Van Dorssaeler A; Burlet-Schiltz O; Schaeffer C; Couté Y; Gonzalez de Peredo A
J Proteomics; 2016 Jan; 132():51-62. PubMed ID: 26585461
[TBL] [Abstract][Full Text] [Related]
7. StatsPro: Systematic integration and evaluation of statistical approaches for detecting differential expression in label-free quantitative proteomics.
Yang Y; Cheng J; Wang S; Yang H
J Proteomics; 2022 Jan; 250():104386. PubMed ID: 34600153
[TBL] [Abstract][Full Text] [Related]
8. Significance analysis of microarray for relative quantitation of LC/MS data in proteomics.
Roxas BA; Li Q
BMC Bioinformatics; 2008 Apr; 9():187. PubMed ID: 18402702
[TBL] [Abstract][Full Text] [Related]
9. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.
Brusniak MY; Bodenmiller B; Campbell D; Cooke K; Eddes J; Garbutt A; Lau H; Letarte S; Mueller LN; Sharma V; Vitek O; Zhang N; Aebersold R; Watts JD
BMC Bioinformatics; 2008 Dec; 9():542. PubMed ID: 19087345
[TBL] [Abstract][Full Text] [Related]
10. A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments.
Matzke MM; Brown JN; Gritsenko MA; Metz TO; Pounds JG; Rodland KD; Shukla AK; Smith RD; Waters KM; McDermott JE; Webb-Robertson BJ
Proteomics; 2013 Feb; 13(3-4):493-503. PubMed ID: 23019139
[TBL] [Abstract][Full Text] [Related]
11. A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry.
Aoshima K; Takahashi K; Ikawa M; Kimura T; Fukuda M; Tanaka S; Parry HE; Fujita Y; Yoshizawa AC; Utsunomiya S; Kajihara S; Tanaka K; Oda Y
BMC Bioinformatics; 2014 Nov; 15(1):376. PubMed ID: 25420746
[TBL] [Abstract][Full Text] [Related]
12. Cross-correlation of spectral count ranking to validate quantitative proteome measurements.
Kannaste O; Suomi T; Salmi J; Uusipaikka E; Nevalainen O; Corthals GL
J Proteome Res; 2014 Apr; 13(4):1957-68. PubMed ID: 24611565
[TBL] [Abstract][Full Text] [Related]
13. DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis.
Zhu Y; Orre LM; Zhou Tran Y; Mermelekas G; Johansson HJ; Malyutina A; Anders S; Lehtiö J
Mol Cell Proteomics; 2020 Jun; 19(6):1047-1057. PubMed ID: 32205417
[TBL] [Abstract][Full Text] [Related]
14. Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data.
Ning K; Fermin D; Nesvizhskii AI
J Proteome Res; 2012 Apr; 11(4):2261-71. PubMed ID: 22329341
[TBL] [Abstract][Full Text] [Related]
15. Enhanced peptide quantification using spectral count clustering and cluster abundance.
Lee S; Kwon MS; Lee HJ; Paik YK; Tang H; Lee JK; Park T
BMC Bioinformatics; 2011 Oct; 12():423. PubMed ID: 22034872
[TBL] [Abstract][Full Text] [Related]
16. An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters.
Li Q; Roxas BA
BMC Bioinformatics; 2009 Feb; 10():43. PubMed ID: 19187558
[TBL] [Abstract][Full Text] [Related]
17. Statistical design and analysis of label-free LC-MS proteomic experiments: a case study of coronary artery disease.
Clough T; Braun S; Fokin V; Ott I; Ragg S; Schadow G; Vitek O
Methods Mol Biol; 2011; 728():293-319. PubMed ID: 21468957
[TBL] [Abstract][Full Text] [Related]
18. On the beta-binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics.
Pham TV; Piersma SR; Warmoes M; Jimenez CR
Bioinformatics; 2010 Feb; 26(3):363-9. PubMed ID: 20007255
[TBL] [Abstract][Full Text] [Related]
19. Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.
Tekwe CD; Carroll RJ; Dabney AR
Bioinformatics; 2012 Aug; 28(15):1998-2003. PubMed ID: 22628520
[TBL] [Abstract][Full Text] [Related]
20. Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: Reproducibility, linearity, and application with complex proteomes.
Wang G; Wu WW; Zeng W; Chou CL; Shen RF
J Proteome Res; 2006 May; 5(5):1214-23. PubMed ID: 16674111
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]