Evidence map›Paper›PMID 20406493›Full record

ArticleBMC systems biology2010

Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis.

Jin Gu, Yang Chen, Shao Li, Yanda Li

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Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Differentially mutated subnetworks discovery.Algorithms for molecular biology : AMB · 2019
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  14. On the performance of de novo pathway enrichment.NPJ systems biology and applications · 2017
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Jin GuMOE Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory for Information Science and Technology (TNLIST) and Department of Automation, Tsinghua University, Beijing 100084, China.
Yang Chen
Shao Li
Yanda Li

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCell responses to environmental stimuli are usually organized as relatively separate responsive gene modules at the molecular level. Identification of responsive gene modules rather than individual differentially expressed (DE) genes will provide important information about the underlying molecular mechanisms. Most of current methods formulate module identification as an optimization problem: find the active sub-networks in the genome-wide gene network by maximizing the objective function considering the gene differential expression and/or the gene-gene co-expression information. Here we presented a new formulation of this task: a group of closely-connected and co-expressed DE genes in the gene network are regarded as the signatures of the underlying responsive gene modules; the modules can be identified by finding the signatures and then recovering the "missing parts" by adding the intermediate genes that connect the DE genes in the gene network.

resultsClustEx, a two-step method based on the new formulation, was developed and applied to identify the responsive gene modules of human umbilical vein endothelial cells (HUVECs) in inflammation and angiogenesis models by integrating the time-course microarray data and genome-wide PPI data. It shows better performance than several available module identification tools by testing on the reference responsive gene sets. Gene set analysis of KEGG pathways, GO terms and microRNAs (miRNAs) target gene sets further supports the ClustEx predictions.

conclusionTaking the closely-connected and co-expressed DE genes in the condition-specific gene network as the signatures of the underlying responsive gene modules provides a new strategy to solve the module identification problem. The identified responsive gene modules of HUVECs and the corresponding enriched pathways/miRNAs provide useful resources for understanding the inflammatory and angiogenic responses of vascular systems.

Indexed as

Gene Expression ProfilingGene Regulatory NetworksMultigene FamilyEndothelium, VascularGene Expression RegulationHumansInflammationMicroRNAsModels, BiologicalModels, GeneticNeovascularization, PathologicOligonucleotide Array Sequence AnalysisSignal TransductionTime FactorsUmbilical VeinsMicroRNAs

Identifiers

PMID20406493
PMCPMC2873318

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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.