Evidence map›Paper›PMID 25436094›Full record

ReviewGastroenterology and hepatology from bed to bench2014

Protein-protein interaction networks (PPI) and complex diseases.

Nahid Safari-Alighiarloo, Mohammad Taghizadeh, Mostafa Rezaei-Tavirani, Bahram Goliaei, Ali Asghar Peyvandi

Abstract readReview
In one paragraph

Review in Gastroenterology and hepatology from bed to bench, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
170citing papers in PubMed, 2 pooled it
–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

170 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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110 more citing papers are in PubMed but not listed here.

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

5 authors.

Nahid Safari-AlighiarlooProteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad TaghizadehBioinformatics Department, Institute of Biochemistry and Biophysics, Tehran University, Tehran, Iran.
Mostafa Rezaei-TaviraniProteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Bahram GoliaeiBioinformatics Department, Institute of Biochemistry and Biophysics, Tehran University, Tehran, Iran.
Ali Asghar PeyvandiHearing Disorders Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network.

Indexed as

Complex diseasesNetworksPPI

Identifiers

PMID25436094
PMCPMC4017556

What OpenQuestion holds

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LicenceCC BY-NC
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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.