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Items: 1 to 20 of 138

1.

Bayesian independent component analysis recovers pathway signatures from blood metabolomics data.

Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ.

J Proteome Res. 2012 Aug 3;11(8):4120-31. doi: 10.1021/pr300231n.

PMID:
22713116
2.

Independent component analysis in non-hypothesis driven metabolomics: improvement of pattern discovery and simplification of biological data interpretation demonstrated with plasma samples of exercising humans.

Li X, Hansen J, Zhao X, Lu X, Weigert C, Häring HU, Pedersen BK, Plomgaard P, Lehmann R, Xu G.

J Chromatogr B Analyt Technol Biomed Life Sci. 2012 Dec 1;910:156-62. doi: 10.1016/j.jchromb.2012.06.030.

PMID:
22809791
3.

MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics.

Liu Y, Smirnov K, Lucio M, Gougeon RD, Alexandre H, Schmitt-Kopplin P.

BMC Bioinformatics. 2016 Mar 2;17:114. doi: 10.1186/s12859-016-0970-4.

4.

Linked independent component analysis for multimodal data fusion.

Groves AR, Beckmann CF, Smith SM, Woolrich MW.

Neuroimage. 2011 Feb 1;54(3):2198-217. doi: 10.1016/j.neuroimage.2010.09.073.

PMID:
20932919
5.

Metabolomics data exploration guided by prior knowledge.

van den Berg RA, Rubingh CM, Westerhuis JA, van der Werf MJ, Smilde AK.

Anal Chim Acta. 2009 Oct 5;651(2):173-81. doi: 10.1016/j.aca.2009.08.029.

PMID:
19782808
6.

Analysis of fMRI data by blind separation into independent spatial components.

McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ.

Hum Brain Mapp. 1998;6(3):160-88.

PMID:
9673671
7.

Knowledge-guided multi-scale independent component analysis for biomarker identification.

Chen L, Xuan J, Wang C, Shih IeM, Wang Y, Zhang Z, Hoffman E, Clarke R.

BMC Bioinformatics. 2008 Oct 6;9:416. doi: 10.1186/1471-2105-9-416.

8.

Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

Blasco H, Błaszczyński J, Billaut JC, Nadal-Desbarats L, Pradat PF, Devos D, Moreau C, Andres CR, Emond P, Corcia P, Słowiński R.

J Biomed Inform. 2015 Feb;53:291-9. doi: 10.1016/j.jbi.2014.12.001.

9.

Metabolomics of medicinal plants: the importance of multivariate analysis of analytical chemistry data.

Okada T, Afendi FM, Altaf-Ul-Amin M, Takahashi H, Nakamura K, Kanaya S.

Curr Comput Aided Drug Des. 2010 Sep;6(3):179-96. Review.

PMID:
20550511
10.

Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets.

Yao F, Coquery J, Lê Cao KA.

BMC Bioinformatics. 2012 Feb 3;13:24. doi: 10.1186/1471-2105-13-24.

11.

Multivariate analysis of neuronal interactions in the generalized partial least squares framework: simulations and empirical studies.

Lin FH, McIntosh AR, Agnew JA, Eden GF, Zeffiro TA, Belliveau JW.

Neuroimage. 2003 Oct;20(2):625-42.

PMID:
14568440
12.

Molecular characterization of zebrafish embryogenesis via DNA microarrays and multiplatform time course metabolomics studies.

Soanes KH, Achenbach JC, Burton IW, Hui JP, Penny SL, Karakach TK.

J Proteome Res. 2011 Nov 4;10(11):5102-17. doi: 10.1021/pr2005549.

PMID:
21910437
13.

Dynamic monitoring system for full-scale wastewater treatment plants.

Yoo CK, Lee JM, Lee IB, Vanrolleghem PA.

Water Sci Technol. 2004;50(11):163-71.

PMID:
15685992
15.
16.

Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis.

Yamamoto H, Fujimori T, Sato H, Ishikawa G, Kami K, Ohashi Y.

BMC Bioinformatics. 2014 Feb 21;15:51. doi: 10.1186/1471-2105-15-51.

17.

Multivariate modeling strategy for intercompartmental analysis of tissue and plasma 1H NMR spectrotypes.

Montoliu I, Martin FP, Collino S, Rezzi S, Kochhar S.

J Proteome Res. 2009 May;8(5):2397-406. doi: 10.1021/pr8010205.

PMID:
19317465
18.

Source density-driven independent component analysis approach for fMRI data.

Hong B, Pearlson GD, Calhoun VD.

Hum Brain Mapp. 2005 Jul;25(3):297-307.

PMID:
15832316
19.

A new strategy of exploring metabolomics data using Monte Carlo tree.

Cao DS, Wang B, Zeng MM, Liang YZ, Xu QS, Zhang LX, Li HD, Hu QN.

Analyst. 2011 Mar 7;136(5):947-54. doi: 10.1039/c0an00383b.

PMID:
21157593
20.

Nuclear magnetic resonance-based metabonomics reveals strong sex effect on plasma metabolism in 17-year-old Scandinavians and correlation to retrospective infant plasma parameters.

Bertram HC, Duus JØ, Petersen BO, Hoppe C, Larnkjaer A, Schack-Nielsen L, Mølgaard C, Michaelsen KF.

Metabolism. 2009 Jul;58(7):1039-45. doi: 10.1016/j.metabol.2009.03.011.

PMID:
19411084
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