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Items: 1 to 20 of 220

1.

An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data.

Piao Y, Piao M, Park K, Ryu KH.

Bioinformatics. 2012 Dec 15;28(24):3306-15. doi: 10.1093/bioinformatics/bts602. Epub 2012 Oct 11.

2.

Cancer classification from gene expression data by NPPC ensemble.

Ghorai S, Mukherjee A, Sengupta S, Dutta PK.

IEEE/ACM Trans Comput Biol Bioinform. 2011 May-Jun;8(3):659-71. doi: 10.1109/TCBB.2010.36.

PMID:
20479504
3.

An entropy-based gene selection method for cancer classification using microarray data.

Liu X, Krishnan A, Mondry A.

BMC Bioinformatics. 2005 Mar 24;6:76.

4.

Gene selection and classification for cancer microarray data based on machine learning and similarity measures.

Liu Q, Sung AH, Chen Z, Liu J, Chen L, Qiao M, Wang Z, Huang X, Deng Y.

BMC Genomics. 2011 Dec 23;12 Suppl 5:S1. doi: 10.1186/1471-2164-12-S5-S1. Epub 2011 Dec 23.

5.

A novel feature selection approach for biomedical data classification.

Peng Y, Wu Z, Jiang J.

J Biomed Inform. 2010 Feb;43(1):15-23. doi: 10.1016/j.jbi.2009.07.008. Epub 2009 Jul 30.

6.

Improving accuracy for cancer classification with a new algorithm for genes selection.

Zhang H, Wang H, Dai Z, Chen MS, Yuan Z.

BMC Bioinformatics. 2012 Nov 13;13:298. doi: 10.1186/1471-2105-13-298.

7.

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis.

Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S.

Bioinformatics. 2005 Mar 1;21(5):631-43. Epub 2004 Sep 16.

8.

An efficient ensemble learning method for gene microarray classification.

Osareh A, Shadgar B.

Biomed Res Int. 2013;2013:478410. doi: 10.1155/2013/478410. Epub 2013 Aug 14.

9.

Stable feature selection and classification algorithms for multiclass microarray data.

Student S, Fujarewicz K.

Biol Direct. 2012 Oct 2;7:33. doi: 10.1186/1745-6150-7-33.

10.

Classification of microarrays; synergistic effects between normalization, gene selection and machine learning.

Önskog J, Freyhult E, Landfors M, Rydén P, Hvidsten TR.

BMC Bioinformatics. 2011 Oct 7;12:390. doi: 10.1186/1471-2105-12-390.

11.

Gene selection from microarray data for cancer classification--a machine learning approach.

Wang Y, Tetko IV, Hall MA, Frank E, Facius A, Mayer KF, Mewes HW.

Comput Biol Chem. 2005 Feb;29(1):37-46.

PMID:
15680584
12.

An efficient statistical feature selection approach for classification of gene expression data.

Chandra B, Gupta M.

J Biomed Inform. 2011 Aug;44(4):529-35. doi: 10.1016/j.jbi.2011.01.001. Epub 2011 Jan 15.

13.

Tumor classification ranking from microarray data.

Hewett R, Kijsanayothin P.

BMC Genomics. 2008 Sep 16;9 Suppl 2:S21. doi: 10.1186/1471-2164-9-S2-S21.

14.

New algorithms for multi-class cancer diagnosis using tumor gene expression signatures.

Bagirov AM, Ferguson B, Ivkovic S, Saunders G, Yearwood J.

Bioinformatics. 2003 Sep 22;19(14):1800-7.

15.

Ensemble gene selection by grouping for microarray data classification.

Liu H, Liu L, Zhang H.

J Biomed Inform. 2010 Feb;43(1):81-7. doi: 10.1016/j.jbi.2009.08.010. Epub 2009 Aug 20.

16.

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
17.

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.

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20.

Robust feature selection for microarray data based on multicriterion fusion.

Yang F, Mao KZ.

IEEE/ACM Trans Comput Biol Bioinform. 2011 Jul-Aug;8(4):1080-92. doi: 10.1109/TCBB.2010.103.

PMID:
21566255
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