Evidence map›Paper›PMID 26484195›Full record

ArticleGenomics data2015

Using shRNA experiments to validate gene regulatory networks.

Catharina Olsen, Kathleen Fleming, Niall Prendergast, Renee Rubio, Frank Emmert-Streib, Gianluca Bontempi, John Quackenbush, Benjamin Haibe-Kains

Abstract read
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In one paragraph

Article in Genomics data, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Catharina OlsenMachine Learning Group, Université Libre de Bruxelles, Brussels, Belgium ; Interuniversity Institute of Bioinformatics in Brussels (IB) , Belgium.
Kathleen FlemingComputational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.
Niall PrendergastComputational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.
Renee RubioComputational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.
Frank Emmert-StreibComputational Medicine and Statistical Learning Laboratory, Department of Signal Processing, Tampere University of Technology, Korkeakoulunkatu 1, 33720 Tampere, Finland ; Institute of Biosciences and Medical Technology, Biokatu 10, 33520 Tampere, Finland.
Gianluca BontempiMachine Learning Group, Université Libre de Bruxelles, Brussels, Belgium ; Interuniversity Institute of Bioinformatics in Brussels (IB) , Belgium.
John QuackenbushComputational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.
Benjamin Haibe-KainsBioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada ; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

Funding

Integrated approaches to deriving predictive networks from public data sourcesR01LM010129 · NLM · DANA-FARBER CANCER INST · PI QUACKENBUSH, JOHN · 2009 to 2010
$1.4M
NLM NIH HHS R01 LM010129
6 · The paper itself

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.

Indexed as

Colon cancerGene expressionKnock-downMicroarrayshRNA

Identifiers

PMID26484195
PMCPMC4535466

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.