Evidence map›Paper›PMID 22952238›Full record

ReviewThe Journal of biological chemistry2012

Uncovering transcription factor modules using one- and three-dimensional analyses.

Xun Lan, Peggy J Farnham, Victor X Jin

Open access · hybridAbstract readReview
In one paragraph

Review in The Journal of biological chemistry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
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

3 authors at 2 institutions in 1 country.

Xun LanDepartment of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA.
Peggy J Farnham
Victor X Jin
The Ohio State University · USUniversity of Southern California · US

Funding

Production Center for Global Mapping of Regulatory ElementsU54HG004558 · NHGRI · YALE UNIVERSITY · PI SNYDER, MICHAEL P. · 2007 to 2011
$15.4M
NCI NIH HHS CA45250NHGRI NIH HHS 1U54HG004558NHGRI NIH HHS U54 HG004558
6 · The paper itself

Abstract

Transcriptional regulation is a critical mediator of many normal cellular processes, as well as disease progression. Transcription factors (TFs) often co-localize at cis-regulatory elements on the DNA, form protein complexes, and collaboratively regulate gene expression. Machine learning and Bayesian approaches have been used to identify TF modules in a one-dimensional context. However, recent studies using high throughput technologies have shown that TF interactions should also be considered in three-dimensional nuclear space. Here, we describe methods for identifying TF modules and discuss how moving from a one-dimensional to a three-dimensional paradigm, along with integrated experimental and computational approaches, can lead to a better understanding of TF association networks.

Indexed as

Artificial IntelligenceBayes TheoremGene Expression RegulationHumansMultiprotein ComplexesTranscription FactorsMultiprotein ComplexesTranscription Factors

Identifiers

PMID22952238
PMCPMC3438924
OpenAlexW1975176705

What OpenQuestion holds

Textmetadata
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.