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Using shRNA experiments to validate gene regulatory networks

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Using shRNA experiments to validate gene regulatory networks. / Olsen, Catharina; Fleming, Kathleen; Prendergast, Niall; Rubio, Renee; Emmert-Streib, Frank; Bontempi, Gianluca; Quackenbush, John; Haibe-Kains, Benjamin.

In: Genomics Data, Vol. 4, 01.06.2015, p. 123-126.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Olsen, C, Fleming, K, Prendergast, N, Rubio, R, Emmert-Streib, F, Bontempi, G, Quackenbush, J & Haibe-Kains, B 2015, 'Using shRNA experiments to validate gene regulatory networks', Genomics Data, vol. 4, pp. 123-126. https://doi.org/10.1016/j.gdata.2015.03.011

APA

Olsen, C., Fleming, K., Prendergast, N., Rubio, R., Emmert-Streib, F., Bontempi, G., ... Haibe-Kains, B. (2015). Using shRNA experiments to validate gene regulatory networks. Genomics Data, 4, 123-126. https://doi.org/10.1016/j.gdata.2015.03.011

Vancouver

Olsen C, Fleming K, Prendergast N, Rubio R, Emmert-Streib F, Bontempi G et al. Using shRNA experiments to validate gene regulatory networks. Genomics Data. 2015 Jun 1;4:123-126. https://doi.org/10.1016/j.gdata.2015.03.011

Author

Olsen, Catharina ; Fleming, Kathleen ; Prendergast, Niall ; Rubio, Renee ; Emmert-Streib, Frank ; Bontempi, Gianluca ; Quackenbush, John ; Haibe-Kains, Benjamin. / Using shRNA experiments to validate gene regulatory networks. In: Genomics Data. 2015 ; Vol. 4. pp. 123-126.

Bibtex - Download

@article{24b297d697a04ffda5aef57bbb0b5065,
title = "Using shRNA experiments to validate gene regulatory networks",
abstract = "Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field.In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.",
keywords = "Colon cancer, Gene expression, Knock-down, Microarray, ShRNA",
author = "Catharina Olsen and Kathleen Fleming and Niall Prendergast and Renee Rubio and Frank Emmert-Streib and Gianluca Bontempi and John Quackenbush and Benjamin Haibe-Kains",
year = "2015",
month = "6",
day = "1",
doi = "10.1016/j.gdata.2015.03.011",
language = "English",
volume = "4",
pages = "123--126",
journal = "Genomics Data",
issn = "2213-5960",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Using shRNA experiments to validate gene regulatory networks

AU - Olsen, Catharina

AU - Fleming, Kathleen

AU - Prendergast, Niall

AU - Rubio, Renee

AU - Emmert-Streib, Frank

AU - Bontempi, Gianluca

AU - Quackenbush, John

AU - Haibe-Kains, Benjamin

PY - 2015/6/1

Y1 - 2015/6/1

N2 - Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field.In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.

AB - Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field.In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.

KW - Colon cancer

KW - Gene expression

KW - Knock-down

KW - Microarray

KW - ShRNA

U2 - 10.1016/j.gdata.2015.03.011

DO - 10.1016/j.gdata.2015.03.011

M3 - Article

VL - 4

SP - 123

EP - 126

JO - Genomics Data

JF - Genomics Data

SN - 2213-5960

ER -