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Similar articles for PubMed (Select 22868679)

1.

Biomarker identification and cancer classification based on microarray data using Laplace naive Bayes model with mean shrinkage.

Wu MY, Dai DQ, Shi Y, Yan H, Zhang XF.

IEEE/ACM Trans Comput Biol Bioinform. 2012 Nov-Dec;9(6):1649-62.

PMID:
22868679
2.
3.

Gene selection in cancer classification using sparse logistic regression with Bayesian regularization.

Cawley GC, Talbot NL.

Bioinformatics. 2006 Oct 1;22(19):2348-55. Epub 2006 Jul 14.

4.

Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification.

Liang Y, Liu C, Luan XZ, Leung KS, Chan TM, Xu ZB, Zhang H.

BMC Bioinformatics. 2013 Jun 19;14:198. doi: 10.1186/1471-2105-14-198.

5.

Refining gene signatures: a Bayesian approach.

Djebbari A, Labbe A.

BMC Bioinformatics. 2009 Dec 10;10:410. doi: 10.1186/1471-2105-10-410.

6.

A GMM-IG framework for selecting genes as expression panel biomarkers.

Wang M, Chen JY.

Artif Intell Med. 2010 Feb-Mar;48(2-3):75-82. doi: 10.1016/j.artmed.2009.07.006. Epub 2009 Dec 8.

PMID:
20004087
8.

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.

9.

Empirical Bayes screening of many p-values with applications to microarray studies.

Datta S, Datta S.

Bioinformatics. 2005 May 1;21(9):1987-94. Epub 2005 Feb 2.

10.

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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12.

Multi-class clustering of cancer subtypes through SVM based ensemble of pareto-optimal solutions for gene marker identification.

Mukhopadhyay A, Bandyopadhyay S, Maulik U.

PLoS One. 2010 Nov 12;5(11):e13803. doi: 10.1371/journal.pone.0013803.

13.

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
14.

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.

15.

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
16.

Regularized binormal ROC method in disease classification using microarray data.

Ma S, Song X, Huang J.

BMC Bioinformatics. 2006 May 9;7:253.

17.

Regularized gene selection in cancer microarray meta-analysis.

Ma S, Huang J.

BMC Bioinformatics. 2009 Jan 1;10:1. doi: 10.1186/1471-2105-10-1.

18.

A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.

Demichelis F, Magni P, Piergiorgi P, Rubin MA, Bellazzi R.

BMC Bioinformatics. 2006 Nov 24;7:514.

19.

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.

20.

A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma.

Toh SH, Prathipati P, Motakis E, Kwoh CK, Yenamandra SP, Kuznetsov VA.

BMC Genomics. 2011 Nov 30;12 Suppl 3:S24. doi: 10.1186/1471-2164-12-S3-S24. Epub 2011 Nov 30.

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