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Items: 1 to 20 of 577

1.

A comparative study of different machine learning methods on microarray gene expression data.

Pirooznia M, Yang JY, Yang MQ, Deng Y.

BMC Genomics. 2008;9 Suppl 1:S13. doi: 10.1186/1471-2164-9-S1-S13.

2.

Recursive cluster elimination (RCE) for classification and feature selection from gene expression data.

Yousef M, Jung S, Showe LC, Showe MK.

BMC Bioinformatics. 2007 May 2;8:144.

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Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis.

Tang Y, Zhang YQ, Huang Z.

IEEE/ACM Trans Comput Biol Bioinform. 2007 Jul-Sep;4(3):365-81.

PMID:
17666757
6.

MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data.

Zhou X, Tuck DP.

Bioinformatics. 2007 May 1;23(9):1106-14. Erratum in: Bioinformatics. 2007 Aug;23(15):2029.

PMID:
17494773
7.

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets.

Gormley M, Dampier W, Ertel A, Karacali B, Tozeren A.

BMC Bioinformatics. 2007 Oct 26;8:415.

8.

Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction.

Shi P, Ray S, Zhu Q, Kon MA.

BMC Bioinformatics. 2011 Sep 23;12:375. doi: 10.1186/1471-2105-12-375.

9.

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data.

Zhang X, Lu X, Shi Q, Xu XQ, Leung HC, Harris LN, Iglehart JD, Miron A, Liu JS, Wong WH.

BMC Bioinformatics. 2006 Apr 10;7:197.

10.

Classification based upon gene expression data: bias and precision of error rates.

Wood IA, Visscher PM, Mengersen KL.

Bioinformatics. 2007 Jun 1;23(11):1363-70. Epub 2007 Mar 28. Review.

PMID:
17392326
11.

The feature selection bias problem in relation to high-dimensional gene data.

Krawczuk J, Łukaszuk T.

Artif Intell Med. 2016 Jan;66:63-71. doi: 10.1016/j.artmed.2015.11.001. Epub 2015 Nov 14.

PMID:
26674595
12.

CARSVM: a class association rule-based classification framework and its application to gene expression data.

Kianmehr K, Alhajj R.

Artif Intell Med. 2008 Sep;44(1):7-25. doi: 10.1016/j.artmed.2008.05.002. Epub 2008 Jun 30.

PMID:
18586476
13.

SVM-T-RFE: a novel gene selection algorithm for identifying metastasis-related genes in colorectal cancer using gene expression profiles.

Li X, Peng S, Chen J, Lü B, Zhang H, Lai M.

Biochem Biophys Res Commun. 2012 Mar 9;419(2):148-53. doi: 10.1016/j.bbrc.2012.01.087. Epub 2012 Jan 28.

PMID:
22306013
14.

Derivation of an artificial gene to improve classification accuracy upon gene selection.

Seo M, Oh S.

Comput Biol Chem. 2012 Feb;36:1-12. doi: 10.1016/j.compbiolchem.2011.11.002. Epub 2011 Nov 28.

PMID:
22340439
15.

Semisupervised learning for molecular profiling.

Furlanello C, Serafini M, Merler S, Jurman G.

IEEE/ACM Trans Comput Biol Bioinform. 2005 Apr-Jun;2(2):110-8.

PMID:
17044176
16.

A stable gene selection in microarray data analysis.

Yang K, Cai Z, Li J, Lin G.

BMC Bioinformatics. 2006 Apr 27;7:228.

17.

HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data.

Wang Y, Makedon FS, Ford JC, Pearlman J.

Bioinformatics. 2005 Apr 15;21(8):1530-7. Epub 2004 Dec 7.

PMID:
15585531
18.

Gene expression profile class prediction using linear Bayesian classifiers.

Asyali MH.

Comput Biol Med. 2007 Dec;37(12):1690-9. Epub 2007 May 22.

PMID:
17517385
19.

Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data.

Baty F, Bihl MP, Perrière G, Culhane AC, Brutsche MH.

BMC Bioinformatics. 2005 Sep 28;6:239.

20.

Multiple SVM-RFE for gene selection in cancer classification with expression data.

Duan KB, Rajapakse JC, Wang H, Azuaje F.

IEEE Trans Nanobioscience. 2005 Sep;4(3):228-34.

PMID:
16220686

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