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Items: 1 to 20 of 63


Selective of informative metabolites using random forests based on model population analysis.

Huang JH, Yan J, Wu QH, Duarte Ferro M, Yi LZ, Lu HM, Xu QS, Liang YZ.

Talanta. 2013 Dec 15;117:549-55. doi: 10.1016/j.talanta.2013.07.070. Epub 2013 Oct 3.


A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.

Yun YH, Wang WT, Tan ML, Liang YZ, Li HD, Cao DS, Lu HM, Xu QS.

Anal Chim Acta. 2014 Jan 7;807:36-43. doi: 10.1016/j.aca.2013.11.032. Epub 2013 Nov 21.


A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis.

Cao DS, Zeng MM, Yi LZ, Wang B, Xu QS, Hu QN, Zhang LX, Lu HM, Liang YZ.

Anal Chim Acta. 2011 Nov 7;706(1):97-104. doi: 10.1016/j.aca.2011.08.025. Epub 2011 Sep 12.


Pathway analysis using random forests classification and regression.

Pang H, Lin A, Holford M, Enerson BE, Lu B, Lawton MP, Floyd E, Zhao H.

Bioinformatics. 2006 Aug 15;22(16):2028-36. Epub 2006 Jun 29.


Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

Liu W, Bai X, Liu Y, Wang W, Han J, Wang Q, Xu Y, Zhang C, Zhang S, Li X, Ren Z, Zhang J, Li C.

Sci Rep. 2015 Aug 19;5:13192. doi: 10.1038/srep13192.


An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++.

Karpievitch YV, Hill EG, Leclerc AP, Dabney AR, Almeida JS.

PLoS One. 2009 Sep 18;4(9):e7087. doi: 10.1371/journal.pone.0007087.


MetaboAnalyst: a web server for metabolomic data analysis and interpretation.

Xia J, Psychogios N, Young N, Wishart DS.

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W652-60. doi: 10.1093/nar/gkp356. Epub 2009 May 8.


Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease.

Proitsi P, Kim M, Whiley L, Pritchard M, Leung R, Soininen H, Kloszewska I, Mecocci P, Tsolaki M, Vellas B, Sham P, Lovestone S, Powell JF, Dobson RJ, Legido-Quigley C.

Transl Psychiatry. 2015 Jan 13;5:e494. doi: 10.1038/tp.2014.127.


Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery.

Yun YH, Deng BC, Cao DS, Wang WT, Liang YZ.

Anal Chim Acta. 2016 Mar 10;911:27-34. doi: 10.1016/j.aca.2015.12.043. Epub 2016 Jan 7.


Biomarkers for early diagnosis of type 2 diabetic nephropathy: a study based on an integrated biomarker system.

Huang M, Liang Q, Li P, Xia J, Wang Y, Hu P, Jiang Z, He Y, Pang L, Han L, Wang Y, Luo G.

Mol Biosyst. 2013 Aug;9(8):2134-41. doi: 10.1039/c3mb25543c. Epub 2013 May 30.


SNP selection and classification of genome-wide SNP data using stratified sampling random forests.

Wu Q, Ye Y, Liu Y, Ng MK.

IEEE Trans Nanobioscience. 2012 Sep;11(3):216-27. doi: 10.1109/TNB.2012.2214232.


Study on plasmatic metabolomics of Uygur patients with essential hypertension based on nuclear magnetic resonance technique.

Zhong L, Zhang JP, Nuermaimaiti AG, Yunusi KX.

Eur Rev Med Pharmacol Sci. 2014;18(23):3673-80.


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. Epub 2014 Dec 11.


Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models.

Lin Z, Vicente Gonçalves CM, Dai L, Lu HM, Huang JH, Ji H, Wang DS, Yi LZ, Liang YZ.

Anal Chim Acta. 2014 May 27;827:22-7. doi: 10.1016/j.aca.2014.04.008. Epub 2014 Apr 8.


Plasma metabolite profiles of Alzheimer's disease and mild cognitive impairment.

Wang G, Zhou Y, Huang FJ, Tang HD, Xu XH, Liu JJ, Wang Y, Deng YL, Ren RJ, Xu W, Ma JF, Zhang YN, Zhao AH, Chen SD, Jia W.

J Proteome Res. 2014 May 2;13(5):2649-58. doi: 10.1021/pr5000895. Epub 2014 Apr 14.


Modelling short time series in metabolomics: a functional data analysis approach.

Montana G, Berk M, Ebbels T.

Adv Exp Med Biol. 2011;696:307-15. doi: 10.1007/978-1-4419-7046-6_31.


A novel test for IGT utilizing metabolite markers of glucose tolerance.

Cobb J, Eckhart A, Perichon R, Wulff J, Mitchell M, Adam KP, Wolfert R, Button E, Lawton K, Elverson R, Carr B, Sinnott M, Ferrannini E.

J Diabetes Sci Technol. 2015 Jan;9(1):69-76. doi: 10.1177/1932296814553622. Epub 2014 Sep 26.


Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study.

Drogan D, Dunn WB, Lin W, Buijsse B, Schulze MB, Langenberg C, Brown M, Floegel A, Dietrich S, Rolandsson O, Wedge DC, Goodacre R, Forouhi NG, Sharp SJ, Spranger J, Wareham NJ, Boeing H.

Clin Chem. 2015 Mar;61(3):487-97. doi: 10.1373/clinchem.2014.228965. Epub 2014 Dec 18.


Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach.

Gogna N, Krishna M, Oommen AM, Dorai K.

Mol Biosyst. 2015 Feb;11(2):595-606. doi: 10.1039/c4mb00507d. Epub 2014 Dec 3.


Variable selection for binary classification using error rate p-values applied to metabolomics data.

van Reenen M, Reinecke CJ, Westerhuis JA, Venter JH.

BMC Bioinformatics. 2016 Jan 14;17:33. doi: 10.1186/s12859-015-0867-7.

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