111 related articles for article (PubMed ID: 28506863)
1. The Null-Test for peptide identification algorithm in Shotgun proteomics.
Zhang SR; Shan YC; Jiang H; Liu JH; Zhou Y; Zhang LH; Zhang YK
J Proteomics; 2017 Jun; 163():118-125. PubMed ID: 28506863
[TBL] [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; 17(Suppl 18):472. PubMed ID: 28105913
[TBL] [Abstract][Full Text] [Related]
3. Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics.
Madej D; Wu L; Lam H
J Proteome Res; 2022 Feb; 21(2):339-348. PubMed ID: 34989576
[TBL] [Abstract][Full Text] [Related]
4. Targeted Feature Detection for Data-Dependent Shotgun Proteomics.
Weisser H; Choudhary JS
J Proteome Res; 2017 Aug; 16(8):2964-2974. PubMed ID: 28673088
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Instance based algorithm for posterior probability calculation by target-decoy strategy to improve protein identifications.
Jiang X; Dong X; Ye M; Zou H
Anal Chem; 2008 Dec; 80(23):9326-35. PubMed ID: 19551949
[TBL] [Abstract][Full Text] [Related]
7. BuildSummary: using a group-based approach to improve the sensitivity of peptide/protein identification in shotgun proteomics.
Sheng Q; Dai J; Wu Y; Tang H; Zeng R
J Proteome Res; 2012 Mar; 11(3):1494-502. PubMed ID: 22217156
[TBL] [Abstract][Full Text] [Related]
8. In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics.
Audain E; Uszkoreit J; Sachsenberg T; Pfeuffer J; Liang X; Hermjakob H; Sanchez A; Eisenacher M; Reinert K; Tabb DL; Kohlbacher O; Perez-Riverol Y
J Proteomics; 2017 Jan; 150():170-182. PubMed ID: 27498275
[TBL] [Abstract][Full Text] [Related]
9. l2 Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification.
Jian L; Xia Z; Niu X; Liang X; Samir P; Link AJ
IEEE/ACM Trans Comput Biol Bioinform; 2016; 13(4):804-9. PubMed ID: 26394437
[TBL] [Abstract][Full Text] [Related]
10. A new estimation of protein-level false discovery rate.
Wu G; Wan X; Xu B
BMC Genomics; 2018 Aug; 19(Suppl 6):567. PubMed ID: 30367581
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Reverse and Random Decoy Methods for False Discovery Rate Estimation in High Mass Accuracy Peptide Spectral Library Searches.
Zhang Z; Burke M; Mirokhin YA; Tchekhovskoi DV; Markey SP; Yu W; Chaerkady R; Hess S; Stein SE
J Proteome Res; 2018 Feb; 17(2):846-857. PubMed ID: 29281288
[TBL] [Abstract][Full Text] [Related]
13. PepDistiller: A quality control tool to improve the sensitivity and accuracy of peptide identifications in shotgun proteomics.
Li N; Wu S; Zhang C; Chang C; Zhang J; Ma J; Li L; Qian X; Xu P; Zhu Y; He F
Proteomics; 2012 Jun; 12(11):1720-5. PubMed ID: 22623377
[TBL] [Abstract][Full Text] [Related]
14. Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics.
Keich U; Kertesz-Farkas A; Noble WS
J Proteome Res; 2015 Aug; 14(8):3148-61. PubMed ID: 26152888
[TBL] [Abstract][Full Text] [Related]
15. Ursgal, Universal Python Module Combining Common Bottom-Up Proteomics Tools for Large-Scale Analysis.
Kremer LP; Leufken J; Oyunchimeg P; Schulze S; Fufezan C
J Proteome Res; 2016 Mar; 15(3):788-94. PubMed ID: 26709623
[TBL] [Abstract][Full Text] [Related]
16. Transfer posterior error probability estimation for peptide identification.
Yi X; Gong F; Fu Y
BMC Bioinformatics; 2020 May; 21(1):173. PubMed ID: 32366221
[TBL] [Abstract][Full Text] [Related]
17. How to talk about protein-level false discovery rates in shotgun proteomics.
The M; Tasnim A; Käll L
Proteomics; 2016 Sep; 16(18):2461-9. PubMed ID: 27503675
[TBL] [Abstract][Full Text] [Related]
18. ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects.
Zhang Y; Xu T; Shan B; Hart J; Aslanian A; Han X; Zong N; Li H; Choi H; Wang D; Acharya L; Du L; Vogt PK; Ping P; Yates JR
J Proteomics; 2015 Nov; 129():25-32. PubMed ID: 26196237
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. False Discovery Rate Estimation in Proteomics.
Aggarwal S; Yadav AK
Methods Mol Biol; 2016; 1362():119-28. PubMed ID: 26519173
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
