Evidence map›Paper›PMID 21915342›Full record

ArticlePloS one2011

Disease gene interaction pathways: a potential framework for how disease genes associate by disease-risk modules.

Lina Chen, Wan Li, Liangcai Zhang, Hong Wang, Weiming He, Jingxie Tai, Xu Li, Xia Li

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 31% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

8 authors at 2 institutions in 1 country.

Lina ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina@ems.hrbmu.edu.cn
Wan Li
Liangcai Zhang
Hong Wang
Weiming He
Jingxie Tai
Xu Li
Xia Li
Harbin Medical University · CNHarbin Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDisease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as 'a form of biological systems', consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic. METHODOLOGY/PRINCIPAL

findingsIn this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways. CONCLUSIONS/SIGNIFICANCE: Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases.

Indexed as

AlgorithmsCluster AnalysisCoronary Artery DiseaseDatabases, GeneticGene Expression ProfilingGene Regulatory NetworksHumans

Identifiers

PMID21915342
PMCPMC3167857
OpenAlexW2087041514

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.