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Items: 1 to 20 of 280

1.

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.

2.

Feature selection and classification of MAQC-II breast cancer and multiple myeloma microarray gene expression data.

Liu Q, Sung AH, Chen Z, Liu J, Huang X, Deng Y.

PLoS One. 2009 Dec 11;4(12):e8250. doi: 10.1371/journal.pone.0008250.

3.

Comparison of feature selection and classification for MALDI-MS data.

Liu Q, Sung AH, Qiao M, Chen Z, Yang JY, Yang MQ, Huang X, Deng Y.

BMC Genomics. 2009 Jul 7;10 Suppl 1:S3. doi: 10.1186/1471-2164-10-S1-S3.

4.

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.

5.

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.

6.

Recursive feature selection with significant variables of support vectors.

Tsai CA, Huang CH, Chang CW, Chen CH.

Comput Math Methods Med. 2012;2012:712542. doi: 10.1155/2012/712542. Epub 2012 Aug 15.

7.

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.

8.

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
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.

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.

11.
12.

Feature weight estimation for gene selection: a local hyperlinear learning approach.

Cai H, Ruan P, Ng M, Akutsu T.

BMC Bioinformatics. 2014 Mar 14;15:70. doi: 10.1186/1471-2105-15-70.

13.

Supervised learning-based tagSNP selection for genome-wide disease classifications.

Liu Q, Yang J, Chen Z, Yang MQ, Sung AH, Huang X.

BMC Genomics. 2008;9 Suppl 1:S6. doi: 10.1186/1471-2164-9-S1-S6.

14.
15.

Relevant and significant supervised gene clusters for microarray cancer classification.

Maji P, Das C.

IEEE Trans Nanobioscience. 2012 Jun;11(2):161-8. doi: 10.1109/TNB.2012.2193590. Epub 2012 Apr 27.

PMID:
22552589
16.

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.

17.

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
18.

Variable selection using probability density function similarity for support vector machine classification of high-dimensional microarray data.

Tang LJ, Jiang JH, Wu HL, Shen GL, Yu RQ.

Talanta. 2009 Jul 15;79(2):260-7. doi: 10.1016/j.talanta.2009.03.044. Epub 2009 Mar 31.

PMID:
19559875
19.

Dimension reduction with redundant gene elimination for tumor classification.

Zeng XQ, Li GZ, Yang JY, Yang MQ, Wu GF.

BMC Bioinformatics. 2008 May 28;9 Suppl 6:S8. doi: 10.1186/1471-2105-9-S6-S8.

20.

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.

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