Evidence map›Paper›PMID 25390635›Full record

ArticlePloS one2014

Ensemble-based network aggregation improves the accuracy of gene network reconstruction.

Rui Zhong, Jeffrey D Allen, Guanghua Xiao, Yang Xie

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Leaf shape inEcology and evolution · 2020
    Article
  10. Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
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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

4 authors.

Rui ZhongQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Jeffrey D AllenQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Guanghua XiaoQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Yang XieQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.

Funding

Integrative Analysis to Identify Therapeutic Targets for Lung CancerR01CA172211 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI XIAO, GUANGHUA · 2013 to 2017
$1.6M
Predicting Adjuvant Chemotherapy Response in Lung CancerR01CA152301 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI XIE, YANG · 2010 to 2014
$1.5M
NCI NIH HHS 1R01CA172211NCI NIH HHS 5R01CA152301NCI NIH HHS R01 CA152301NCI NIH HHS R01 CA172211
6 · The paper itself

Abstract

Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies - producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled "ENA", accessible on CRAN (http://cran.r-project.org/web/packages/ENA/).

Indexed as

Gene Regulatory NetworksAlgorithmsCarcinoma, Non-Small-Cell LungComputational BiologyComputer SimulationDatabases, GeneticEpithelial-Mesenchymal TransitionEscherichia coliGene Expression Regulation, NeoplasticGenomeHumansLung NeoplasmsOligonucleotide Array Sequence AnalysisReproducibility of ResultsROC CurveSequence Analysis, RNA

Identifiers

PMID25390635
PMCPMC4229114

What OpenQuestion holds

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LicenceCC BY
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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.