Evidence map›Paper›PMID 23166858›Full record

ArticleScientific reports2012

Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes.

Binhua Tang, Hang-Kai Hsu, Pei-Yin Hsu, Russell Bonneville, Su-Shing Chen, Tim H-M Huang, Victor X Jin

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
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

15 citing papers in PubMed, 27 citations in OpenAlex.

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  8. COPAR: A ChIP-Seq Optimal Peak Analyzer.BioMed research international · 2017
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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

7 authors at 3 institutions in 1 country.

Binhua TangDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Hang-Kai Hsu
Pei-Yin Hsu
Russell Bonneville
Su-Shing Chen
Tim H-M Huang
Victor X Jin
The Ohio State University · USThe University of Texas Health Science Center at San Antonio · USUniversity of Florida · US

Funding

Epigenomics of Bisphenol A Exposure and Disease RiskR01ES017594 · NIEHS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI HUANG, TIM H.-M. · 2009 to 2013
$1.8M
NIEHS NIH HHS R01 ES017594
6 · The paper itself

Abstract

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.

Indexed as

Gene Expression Regulation, NeoplasticGene Regulatory NetworksBreast NeoplasmsCell Line, TumorChromatin ImmunoprecipitationEstrogen Receptor alphaFemaleGene Expression ProfilingHumansMCF-7 CellsOligonucleotide Array Sequence AnalysisSignal TransductionTreatment OutcomeEstrogen Receptor alpha

Identifiers

PMID23166858
PMCPMC3500769
OpenAlexW1997792415

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.