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Results: 1 to 20 of 24

1.

Aggregation effect in microarray data analysis.

Chen L, Almudevar A, Klebanov L.

Methods Mol Biol. 2013;972:177-91. doi: 10.1007/978-1-60327-337-4_11.

PMID:
23385538
2.

Gene selection with the δ-sequence method.

Qiu X, Klebanov L.

Methods Mol Biol. 2013;972:57-71. doi: 10.1007/978-1-60327-337-4_4.

PMID:
23385531
3.

First-spike latency in the presence of spontaneous activity.

Pawlas Z, Klebanov LB, Benes V, Prokesová M, Popelár J, Lánský P.

Neural Comput. 2010 Jul;22(7):1675-97. doi: 10.1162/neco.2010.11-09-1118.

PMID:
20235823
4.

Detecting intergene correlation changes in microarray analysis: a new approach to gene selection.

Hu R, Qiu X, Glazko G, Klebanov L, Yakovlev A.

BMC Bioinformatics. 2009 Jan 15;10:20. doi: 10.1186/1471-2105-10-20.

5.

A nitty-gritty aspect of correlation and network inference from gene expression data.

Klebanov LB, Yakovlev AY.

Biol Direct. 2008 Aug 20;3:35. doi: 10.1186/1745-6150-3-35. Review.

6.

Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype.

McMurray HR, Sampson ER, Compitello G, Kinsey C, Newman L, Smith B, Chen SR, Klebanov L, Salzman P, Yakovlev A, Land H.

Nature. 2008 Jun 19;453(7198):1112-6. doi: 10.1038/nature06973. Epub 2008 May 25.

7.

Testing differential expression in nonoverlapping gene pairs: a new perspective for the empirical Bayes method.

Klebanov L, Qiu X, Yakovlev A.

J Bioinform Comput Biol. 2008 Apr;6(2):301-16.

PMID:
18464324
8.

Dr. Andrei Yakovlev: visionary, leader, iconoclast.

Gusev Y, Hanin L, Klebanov L, Tsodikov A, Yanev NM, Zorin A.

Biol Direct. 2008 Mar 26;3:10. doi: 10.1186/1745-6150-3-10. No abstract available.

9.

Parameters of spike trains observed in a short time window.

Pawlas Z, Klebanov LB, Prokop M, Lansky P.

Neural Comput. 2008 May;20(5):1325-43. doi: 10.1162/neco.2007.01-07-442.

PMID:
18194105
11.

A multivariate extension of the gene set enrichment analysis.

Klebanov L, Glazko G, Salzman P, Yakovlev A, Xiao Y.

J Bioinform Comput Biol. 2007 Oct;5(5):1139-53.

PMID:
17933015
12.

Is there an alternative to increasing the sample size in microarray studies?

Klebanov L, Yakovlev A.

Bioinformation. 2007 Apr 10;1(10):429-31.

13.

How high is the level of technical noise in microarray data?

Klebanov L, Yakovlev A.

Biol Direct. 2007 Apr 11;2:9.

14.

Statistical methods and microarray data.

Klebanov L, Qiu X, Welle S, Yakovlev A.

Nat Biotechnol. 2007 Jan;25(1):25-6; author reply 26-7. No abstract available.

PMID:
17211383
15.

Utility of correlation measures in analysis of gene expression.

Almudevar A, Klebanov LB, Qiu X, Salzman P, Yakovlev AY.

NeuroRx. 2006 Jul;3(3):384-95. Review.

16.

Treating expression levels of different genes as a sample in microarray data analysis: is it worth a risk?

Klebanov L, Yakovlev A.

Stat Appl Genet Mol Biol. 2006;5:Article9. Epub 2006 Mar 24.

PMID:
16646873
17.

A new type of stochastic dependence revealed in gene expression data.

Klebanov L, Jordan C, Yakovlev A.

Stat Appl Genet Mol Biol. 2006;5:Article7. Epub 2006 Mar 6. Erratum in: Stat Appl Genet Mol Biol. 2006;5(1):Article 7.

PMID:
16646871
18.

Correlation between gene expression levels and limitations of the empirical bayes methodology for finding differentially expressed genes.

Qiu X, Klebanov L, Yakovlev A.

Stat Appl Genet Mol Biol. 2005;4:Article34. Epub 2005 Nov 22.

PMID:
16646853
19.

The effects of normalization on the correlation structure of microarray data.

Qiu X, Brooks AI, Klebanov L, Yakovlev N.

BMC Bioinformatics. 2005 May 16;6:120.

20.

Multivariate search for differentially expressed gene combinations.

Xiao Y, Frisina R, Gordon A, Klebanov L, Yakovlev A.

BMC Bioinformatics. 2004 Oct 26;5:164.

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