159 related articles for article (PubMed ID: 33104352)
1. Phenotype Classification using Proteome Data in a Data-Independent Acquisition Tensor Format.
Zhang F; Yu S; Wu L; Zang Z; Yi X; Zhu J; Lu C; Sun P; Sun Y; Selvarajan S; Chen L; Teng X; Zhao Y; Wang G; Xiao J; Huang S; Kon OL; Iyer NG; Li SZ; Luan Z; Guo T
J Am Soc Mass Spectrom; 2020 Nov; 31(11):2296-2304. PubMed ID: 33104352
[TBL] [Abstract][Full Text] [Related]
2. MSSort-DIA
Li Y; He Q; Guo H; Zhong CQ; Li X; Li Y; Han J; Shuai J
J Proteomics; 2022 May; 259():104542. PubMed ID: 35231660
[TBL] [Abstract][Full Text] [Related]
3. In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics.
Yang Y; Liu X; Shen C; Lin Y; Yang P; Qiao L
Nat Commun; 2020 Jan; 11(1):146. PubMed ID: 31919359
[TBL] [Abstract][Full Text] [Related]
4. Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries.
Van Puyvelde B; Willems S; Gabriels R; Daled S; De Clerck L; Vande Casteele S; Staes A; Impens F; Deforce D; Martens L; Degroeve S; Dhaenens M
Proteomics; 2020 Feb; 20(3-4):e1900306. PubMed ID: 31981311
[TBL] [Abstract][Full Text] [Related]
5. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.
Demichev V; Messner CB; Vernardis SI; Lilley KS; Ralser M
Nat Methods; 2020 Jan; 17(1):41-44. PubMed ID: 31768060
[TBL] [Abstract][Full Text] [Related]
6. Hybrid data acquisition and processing strategies with increased throughput and selectivity: pSMART analysis for global qualitative and quantitative analysis.
Prakash A; Peterman S; Ahmad S; Sarracino D; Frewen B; Vogelsang M; Byram G; Krastins B; Vadali G; Lopez M
J Proteome Res; 2014 Dec; 13(12):5415-30. PubMed ID: 25244318
[TBL] [Abstract][Full Text] [Related]
7. Avant-garde: an automated data-driven DIA data curation tool.
Vaca Jacome AS; Peckner R; Shulman N; Krug K; DeRuff KC; Officer A; Christianson KE; MacLean B; MacCoss MJ; Carr SA; Jaffe JD
Nat Methods; 2020 Dec; 17(12):1237-1244. PubMed ID: 33199889
[TBL] [Abstract][Full Text] [Related]
8. Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors.
Ge W; Liang X; Zhang F; Hu Y; Xu L; Xiang N; Sun R; Liu W; Xue Z; Yi X; Sun Y; Wang B; Zhu J; Lu C; Zhan X; Chen L; Wu Y; Zheng Z; Gong W; Wu Q; Yu J; Ye Z; Teng X; Huang S; Zheng S; Liu T; Yuan C; Guo T
J Proteome Res; 2021 Dec; 20(12):5392-5401. PubMed ID: 34748352
[TBL] [Abstract][Full Text] [Related]
9. MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics.
Isaksson M; Karlsson C; Laurell T; Kirkeby A; Heusel M
J Proteome Res; 2022 Feb; 21(2):535-546. PubMed ID: 35042333
[TBL] [Abstract][Full Text] [Related]
10. A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry.
Zhang F; Ge W; Huang L; Li D; Liu L; Dong Z; Xu L; Ding X; Zhang C; Sun Y; A J; Gao J; Guo T
Mol Cell Proteomics; 2023 Sep; 22(9):100623. PubMed ID: 37481071
[TBL] [Abstract][Full Text] [Related]
11. Software for quantitative proteomic analysis using stable isotope labeling and data independent acquisition.
Huang X; Liu M; Nold MJ; Tian C; Fu K; Zheng J; Geromanos SJ; Ding SJ
Anal Chem; 2011 Sep; 83(18):6971-9. PubMed ID: 21834580
[TBL] [Abstract][Full Text] [Related]
12. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling.
Chapman JD; Goodlett DR; Masselon CD
Mass Spectrom Rev; 2014; 33(6):452-70. PubMed ID: 24281846
[TBL] [Abstract][Full Text] [Related]
13. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023.
Lou R; Shui W
Mol Cell Proteomics; 2024 Feb; 23(2):100712. PubMed ID: 38182042
[TBL] [Abstract][Full Text] [Related]
14. Deep representation features from DreamDIA
Gao M; Yang W; Li C; Chang Y; Liu Y; He Q; Zhong CQ; Shuai J; Yu R; Han J
Commun Biol; 2021 Oct; 4(1):1190. PubMed ID: 34650228
[TBL] [Abstract][Full Text] [Related]
15. Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry.
Cho KC; Clark DJ; Schnaubelt M; Teo GC; Leprevost FDV; Bocik W; Boja ES; Hiltke T; Nesvizhskii AI; Zhang H
Anal Chem; 2020 Mar; 92(6):4217-4225. PubMed ID: 32058701
[TBL] [Abstract][Full Text] [Related]
16.
Chen M; Zhu P; Wan Q; Ruan X; Wu P; Hao Y; Zhang Z; Sun J; Nie W; Chen S
Anal Chem; 2023 May; 95(19):7495-7502. PubMed ID: 37126374
[TBL] [Abstract][Full Text] [Related]
17. Data-independent acquisition proteomics methods for analyzing post-translational modifications.
Yang Y; Qiao L
Proteomics; 2023 Apr; 23(7-8):e2200046. PubMed ID: 36036492
[TBL] [Abstract][Full Text] [Related]
18. Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM-DIA, and Untargeted DIA.
Koopmans F; Ho JTC; Smit AB; Li KW
Proteomics; 2018 Jan; 18(1):. PubMed ID: 29134766
[TBL] [Abstract][Full Text] [Related]
19. High throughput and accurate serum proteome profiling by integrated sample preparation technology and single-run data independent mass spectrometry analysis.
Lin L; Zheng J; Yu Q; Chen W; Xing J; Chen C; Tian R
J Proteomics; 2018 Mar; 174():9-16. PubMed ID: 29278786
[TBL] [Abstract][Full Text] [Related]
20. New targeted approaches for the quantification of data-independent acquisition mass spectrometry.
Bruderer R; Sondermann J; Tsou CC; Barrantes-Freer A; Stadelmann C; Nesvizhskii AI; Schmidt M; Reiter L; Gomez-Varela D
Proteomics; 2017 May; 17(9):. PubMed ID: 28319648
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
