Evidence map›Paper›PMID 23324335›Full record

ArticleAlgorithms for molecular biology : AMB2013

Using graph models to find transcription factor modules: the hitting set problem and an exact algorithm.

Songjian Lu, Xinghua Lu

Abstract read
In one paragraph

Article in Algorithms for molecular biology : AMB, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Songjian LuDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15219, USA. songjian@pitt.edu.
Xinghua Lu

Funding

Statistical methods for integromics discoveriesR01LM010144 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LU, XINGHUA · 2009 to 2013
$1.6M
Ontology-Driven Methods for Knowledge Acquisition and Knowledge DiscoveryR01LM011155 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LU, XINGHUA · 2011 to 2014
$1.2M
NLM NIH HHS R01 LM010144NLM NIH HHS R01 LM011155
6 · The paper itself

Abstract

: Systematically perturbing a cellular system and monitoring the effects of the perturbations on gene expression provide a powerful approach to study signal transduction in gene expression systems. A critical step of revealing a signal transduction pathway regulating gene expression is to identify transcription factors transmitting signals in the system. In this paper, we address the task of identifying modules of cooperative transcription factors based on results derived from systems-biology experiments at two levels: First, a graph algorithm is developed to identify a minimum set of co-operative TFs that covers the differentially expressed genes under each systematic perturbation. Second, using a clique-finding approach, modules of TFs that tend to consistently cooperate together under various perturbations are further identified. Our results indicate that this approach is capable of identifying many known TF modules based on the individual experiment; thus we provide a novel graph-based method of identifying context-specific and highly reused TF-modules.

Identifiers

PMID23324335
PMCPMC3622577

What OpenQuestion holds

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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.