182 related articles for article (PubMed ID: 37071700)
21. Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets.
Climaco Pinto R; Karaman I; Lewis MR; Hällqvist J; Kaluarachchi M; Graça G; Chekmeneva E; Durainayagam B; Ghanbari M; Ikram MA; Zetterberg H; Griffin J; Elliott P; Tzoulaki I; Dehghan A; Herrington D; Ebbels T
Anal Chem; 2022 Apr; 94(14):5493-5503. PubMed ID: 35360896
[TBL] [Abstract][Full Text] [Related]
22. Automated optimization of XCMS parameters for improved peak picking of liquid chromatography-mass spectrometry data using the coefficient of variation and parameter sweeping for untargeted metabolomics.
Manier SK; Keller A; Meyer MR
Drug Test Anal; 2019 Jun; 11(6):752-761. PubMed ID: 30479047
[TBL] [Abstract][Full Text] [Related]
23. Enhancing Metabolome Coverage in Data-Dependent LC-MS/MS Analysis through an Integrated Feature Extraction Strategy.
Hu Y; Cai B; Huan T
Anal Chem; 2019 Nov; 91(22):14433-14441. PubMed ID: 31626534
[TBL] [Abstract][Full Text] [Related]
24. Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data.
Myers OD; Sumner SJ; Li S; Barnes S; Du X
Anal Chem; 2017 Sep; 89(17):8689-8695. PubMed ID: 28752757
[TBL] [Abstract][Full Text] [Related]
25. PeakBot: machine-learning-based chromatographic peak picking.
Bueschl C; Doppler M; Varga E; Seidl B; Flasch M; Warth B; Zanghellini J
Bioinformatics; 2022 Jun; 38(13):3422-3428. PubMed ID: 35604083
[TBL] [Abstract][Full Text] [Related]
26. Comparison of peak-picking workflows for untargeted liquid chromatography/high-resolution mass spectrometry metabolomics data analysis.
Rafiei A; Sleno L
Rapid Commun Mass Spectrom; 2015 Jan; 29(1):119-27. PubMed ID: 25462372
[TBL] [Abstract][Full Text] [Related]
27. CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics.
Mathema VB; Duangkumpha K; Wanichthanarak K; Jariyasopit N; Dhakal E; Sathirapongsasuti N; Kitiyakara C; Sirivatanauksorn Y; Khoomrung S
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35022651
[TBL] [Abstract][Full Text] [Related]
28. Deep neural network for detecting arbitrary precision peptide features through attention based segmentation.
Zohora FT; Rahman MZ; Tran NH; Xin L; Shan B; Li M
Sci Rep; 2021 Sep; 11(1):18249. PubMed ID: 34521906
[TBL] [Abstract][Full Text] [Related]
29. MS-CleanR: A Feature-Filtering Workflow for Untargeted LC-MS Based Metabolomics.
Fraisier-Vannier O; Chervin J; Cabanac G; Puech V; Fournier S; Durand V; Amiel A; André O; Benamar OA; Dumas B; Tsugawa H; Marti G
Anal Chem; 2020 Jul; 92(14):9971-9981. PubMed ID: 32589017
[TBL] [Abstract][Full Text] [Related]
30. Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection.
Wu CT; Wang Y; Wang Y; Ebbels T; Karaman I; Graça G; Pinto R; Herrington DM; Wang Y; Yu G
Bioinformatics; 2020 May; 36(9):2862-2871. PubMed ID: 31950989
[TBL] [Abstract][Full Text] [Related]
31. 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]
32. Dinosaur: A Refined Open-Source Peptide MS Feature Detector.
Teleman J; Chawade A; Sandin M; Levander F; Malmström J
J Proteome Res; 2016 Jul; 15(7):2143-51. PubMed ID: 27224449
[TBL] [Abstract][Full Text] [Related]
33. Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples.
Fan Y; Yu C; Lu H; Chen Y; Hu B; Zhang X; Su J; Zhang Z
J Chromatogr A; 2023 Feb; 1690():463768. PubMed ID: 36641940
[TBL] [Abstract][Full Text] [Related]
34. A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma.
Li L; Mei Z; Li Y; Yu Y; Liu M
Comput Biol Med; 2024 Jun; 175():108519. PubMed ID: 38688128
[TBL] [Abstract][Full Text] [Related]
35. Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics.
Yu YJ; Zheng QX; Zhang YM; Zhang Q; Zhang YY; Liu PP; Lu P; Fan MJ; Chen QS; Bai CC; Fu HY; She Y
J Chromatogr A; 2019 Jan; 1585():172-181. PubMed ID: 30509617
[TBL] [Abstract][Full Text] [Related]
36. metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.
Habra H; Meijer JL; Shen T; Fiehn O; Gaul DA; Fernández FM; Rempfert KR; Metz TO; Peterson KE; Evans CR; Karnovsky A
Metabolites; 2024 Feb; 14(2):. PubMed ID: 38393017
[TBL] [Abstract][Full Text] [Related]
37. ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets.
Gorrochategui E; Jaumot J; Tauler R
BMC Bioinformatics; 2019 May; 20(1):256. PubMed ID: 31101001
[TBL] [Abstract][Full Text] [Related]
38. Detection, segmentation, and 3D pose estimation of surgical tools using convolutional neural networks and algebraic geometry.
Hasan MK; Calvet L; Rabbani N; Bartoli A
Med Image Anal; 2021 May; 70():101994. PubMed ID: 33611053
[TBL] [Abstract][Full Text] [Related]
39. Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis.
Li B; Tang J; Yang Q; Cui X; Li S; Chen S; Cao Q; Xue W; Chen N; Zhu F
Sci Rep; 2016 Dec; 6():38881. PubMed ID: 27958387
[TBL] [Abstract][Full Text] [Related]
40. lcmsWorld: High-Performance 3D Visualization Software for Mass Spectrometry.
McCabe A; Jones AR
J Proteome Res; 2021 Apr; 20(4):1981-1985. PubMed ID: 33710902
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
