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Items: 1 to 20 of 258

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.

PMID:
23060613
2.

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.

3.

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.

PMID:
15374862
4.

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
5.

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.

6.

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.

7.

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.

PMID:
14512351
8.

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.

9.
10.

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
11.

Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

Martinez E, Alvarez MM, Trevino V.

Comput Biol Chem. 2010 Aug;34(4):244-50. doi: 10.1016/j.compbiolchem.2010.08.003. Epub 2010 Sep 9.

PMID:
20888301
12.

Tumor classification based on non-negative matrix factorization using gene expression data.

Zheng CH, Ng TY, Zhang L, Shiu CK, Wang HQ.

IEEE Trans Nanobioscience. 2011 Jun;10(2):86-93. doi: 10.1109/TNB.2011.2144998. Epub 2011 Jul 7.

PMID:
21742573
13.
14.

Outcome prediction based on microarray analysis: a critical perspective on methods.

Zervakis M, Blazadonakis ME, Tsiliki G, Danilatou V, Tsiknakis M, Kafetzopoulos D.

BMC Bioinformatics. 2009 Feb 7;10:53. doi: 10.1186/1471-2105-10-53.

15.

A granular computing approach to gene selection.

Sun L, Xu J.

Biomed Mater Eng. 2014;24(1):1307-14. doi: 10.3233/BME-130933.

PMID:
24212026
16.

Ensemble machine learning on gene expression data for cancer classification.

Tan AC, Gilbert D.

Appl Bioinformatics. 2003;2(3 Suppl):S75-83.

PMID:
15130820
17.

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
18.

Hybrid genetic algorithm-neural network: feature extraction for unpreprocessed microarray data.

Tong DL, Schierz AC.

Artif Intell Med. 2011 Sep;53(1):47-56. doi: 10.1016/j.artmed.2011.06.008. Epub 2011 Jul 19.

PMID:
21775110
19.

LS Bound based gene selection for DNA microarray data.

Zhou X, Mao KZ.

Bioinformatics. 2005 Apr 15;21(8):1559-64. Epub 2004 Dec 14.

PMID:
15598834
20.

A greedy algorithm for gene selection based on SVM and correlation.

Song M, Rajasekaran S.

Int J Bioinform Res Appl. 2010;6(3):296-307.

PMID:
20615837

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