Vinciotti V - Search Results

1

ℓ 1-Penalized censored Gaussian graphical model.

Augugliaro L, Abbruzzo A, Vinciotti V. Augugliaro L, et al. Among authors: vinciotti v. Biostatistics. 2020 Apr 1;21(2):e1-e16. doi: 10.1093/biostatistics/kxy043. Biostatistics. 2020. PMID: 30203001

2

Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

Vinciotti V, Augugliaro L, Abbruzzo A, Wit EC. Vinciotti V, et al. Stat Appl Genet Mol Biol. 2016 Jun 1;15(3):193-212. doi: 10.1515/sagmb-2014-0075. Stat Appl Genet Mol Biol. 2016. PMID: 27023322

3

Joint modeling of ChIP-seq data via a Markov random field model.

Bao Y, Vinciotti V, Wit E, 't Hoen PA. Bao Y, et al. Among authors: vinciotti v. Biostatistics. 2014 Apr;15(2):296-310. doi: 10.1093/biostatistics/kxt047. Epub 2013 Oct 30. Biostatistics. 2014. PMID: 24178187

4

NEAT: an efficient network enrichment analysis test.

Signorelli M, Vinciotti V, Wit EC. Signorelli M, et al. Among authors: vinciotti v. BMC Bioinformatics. 2016 Sep 5;17(1):352. doi: 10.1186/s12859-016-1203-6. BMC Bioinformatics. 2016. PMID: 27597310 Free PMC article.

5

An experimental evaluation of a loop versus a reference design for two-channel microarrays.

Vinciotti V, Khanin R, D'Alimonte D, Liu X, Cattini N, Hotchkiss G, Bucca G, de Jesus O, Rasaiyaah J, Smith CP, Kellam P, Wit E. Vinciotti V, et al. Bioinformatics. 2005 Feb 15;21(4):492-501. doi: 10.1093/bioinformatics/bti022. Epub 2004 Sep 16. Bioinformatics. 2005. PMID: 15374872

6

An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series.

Wang Z, Liu X, Liu Y, Liang J, Vinciotti V. Wang Z, et al. Among authors: vinciotti v. IEEE/ACM Trans Comput Biol Bioinform. 2009 Jul-Sep;6(3):410-9. doi: 10.1109/TCBB.2009.5. IEEE/ACM Trans Comput Biol Bioinform. 2009. PMID: 19644169

7

Consistency of biological networks inferred from microarray and sequencing data.

Vinciotti V, Wit EC, Jansen R, de Geus EJ, Penninx BW, Boomsma DI, 't Hoen PA. Vinciotti V, et al. BMC Bioinformatics. 2016 Jun 24;17:254. doi: 10.1186/s12859-016-1136-0. BMC Bioinformatics. 2016. PMID: 27342572 Free PMC article.

8

Exploiting the full power of temporal gene expression profiling through a new statistical test: application to the analysis of muscular dystrophy data.

Vinciotti V, Liu X, Turk R, de Meijer EJ, 't Hoen PA. Vinciotti V, et al. BMC Bioinformatics. 2006 Apr 3;7:183. doi: 10.1186/1471-2105-7-183. BMC Bioinformatics. 2006. PMID: 16584545 Free PMC article.

9

A Simple and Adaptive Dispersion Regression Model for Count Data.

Klakattawi HS, Vinciotti V, Yu K. Klakattawi HS, et al. Among authors: vinciotti v. Entropy (Basel). 2018 Feb 22;20(2):142. doi: 10.3390/e20020142. Entropy (Basel). 2018. PMID: 33265233 Free PMC article.

10

Consensus clustering and functional interpretation of gene-expression data.

Swift S, Tucker A, Vinciotti V, Martin N, Orengo C, Liu X, Kellam P. Swift S, et al. Among authors: vinciotti v. Genome Biol. 2004;5(11):R94. doi: 10.1186/gb-2004-5-11-r94. Epub 2004 Nov 1. Genome Biol. 2004. PMID: 15535870 Free PMC article.

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