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Items: 1 to 20 of 99

1.

An approach for extensibly profiling the molecular states of cellular subpopulations.

Loo LH, Lin HJ, Steininger RJ 3rd, Wang Y, Wu LF, Altschuler SJ.

Nat Methods. 2009 Oct;6(10):759-65. doi: 10.1038/nmeth.1375.

2.

A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments.

Robinson MD, De Souza DP, Keen WW, Saunders EC, McConville MJ, Speed TP, Likić VA.

BMC Bioinformatics. 2007 Oct 29;8:419.

3.

Multiclass cancer classification and biomarker discovery using GA-based algorithms.

Liu JJ, Cutler G, Li W, Pan Z, Peng S, Hoey T, Chen L, Ling XB.

Bioinformatics. 2005 Jun 1;21(11):2691-7.

4.

Automated discovery of functional generality of human gene expression programs.

Gerber GK, Dowell RD, Jaakkola TS, Gifford DK.

PLoS Comput Biol. 2007 Aug;3(8):e148.

5.

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.

6.

Clustering-based spot segmentation of cDNA microarray images.

Uslan V, Bucak IÖ.

Conf Proc IEEE Eng Med Biol Soc. 2010;2010:1828-31. doi: 10.1109/IEMBS.2010.5626430.

PMID:
21096143
7.

Towards automated cellular image segmentation for RNAi genome-wide screening.

Zhou X, Liu KY, Bradley P, Perrimon N, Wong ST.

Med Image Comput Comput Assist Interv. 2005;8(Pt 1):885-92.

PMID:
16685930
8.

A blocking strategy to improve gene selection for classification of gene expression data.

Bontempi G.

IEEE/ACM Trans Comput Biol Bioinform. 2007 Apr-Jun;4(2):293-300.

PMID:
17473321
9.
10.

Knowledge-based gene expression classification via matrix factorization.

Schachtner R, Lutter D, Knollmüller P, Tomé AM, Theis FJ, Schmitz G, Stetter M, Vilda PG, Lang EW.

Bioinformatics. 2008 Aug 1;24(15):1688-97. doi: 10.1093/bioinformatics/btn245.

11.

A Bayesian approach to joint feature selection and classifier design.

Krishnapuram B, Hartemink AJ, Carin L, Figueiredo MA.

IEEE Trans Pattern Anal Mach Intell. 2004 Sep;26(9):1105-11.

PMID:
15742887
13.

Guilt-by-association feature selection: identifying biomarkers from proteomic profiles.

Shin H, Sheu B, Joseph M, Markey MK.

J Biomed Inform. 2008 Feb;41(1):124-36.

14.

Multiobjective optimization in bioinformatics and computational biology.

Handl J, Kell DB, Knowles J.

IEEE/ACM Trans Comput Biol Bioinform. 2007 Apr-Jun;4(2):279-92. Review.

PMID:
17473320
15.

Comparison of unsupervised and supervised gene selection methods.

Herold D, Lutter D, Schachtner R, Tome AM, Schmitz G, Lang EW.

Conf Proc IEEE Eng Med Biol Soc. 2008;2008:5212-5. doi: 10.1109/IEMBS.2008.4650389.

PMID:
19163892
16.

A stable iterative method for refining discriminative gene clusters.

Xu M, Zhu M, Zhang L.

BMC Genomics. 2008 Sep 16;9 Suppl 2:S18. doi: 10.1186/1471-2164-9-S2-S18.

17.

Small, fuzzy and interpretable gene expression based classifiers.

Vinterbo SA, Kim EY, Ohno-Machado L.

Bioinformatics. 2005 May 1;21(9):1964-70.

18.

Combined static and dynamic analysis for determining the quality of time-series expression profiles.

Simon I, Siegfried Z, Ernst J, Bar-Joseph Z.

Nat Biotechnol. 2005 Dec;23(12):1503-8.

PMID:
16333294
19.

How does gene expression clustering work?

D'haeseleer P.

Nat Biotechnol. 2005 Dec;23(12):1499-501. Review.

PMID:
16333293
20.

A knowledge-driven approach to cluster validity assessment.

Bolshakova N, Azuaje F, Cunningham P.

Bioinformatics. 2005 May 15;21(10):2546-7.

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