Evidence map›Paper›PMID 22121215›Full record

ArticleNucleic acids research2012

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets.

Daniel Hurley, Hiromitsu Araki, Yoshinori Tamada, Ben Dunmore, Deborah Sanders, Sally Humphreys, Muna Affara, Seiya Imoto, Kaori Yasuda, Yuki Tomiyasu and 9 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

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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

19 authors.

Daniel HurleyAuckland Bioengineering Institute, Department of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Hiromitsu Araki
Yoshinori Tamada
Ben Dunmore
Deborah Sanders
Sally Humphreys
Muna Affara
Seiya Imoto
Kaori Yasuda
Yuki Tomiyasu
Kosuke Tashiro
Christopher Savoie
Vicky Cho
Stephen Smith
Satoru Kuhara
Satoru Miyano
D Stephen Charnock-Jones
Edmund J Crampin
Cristin G Print

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

Indexed as

Gene Expression ProfilingGene Regulatory NetworksSoftwareCells, CulturedComputer GraphicsHumansHuman Umbilical Vein Endothelial CellsNF-kappa BNF-kappa B p50 SubunitOligonucleotide Array Sequence AnalysisRNA, Small InterferingTumor Necrosis Factor-alphaNF-kappa BNF-kappa B p50 SubunitRNA, Small InterferingTumor Necrosis Factor-alpha

Identifiers

PMID22121215
PMCPMC3315333

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.