Evidence map›Paper›PMID 25861037›Full record

ArticleBMC bioinformatics2015

A comparative study of disease genes and drug targets in the human protein interactome.

Jingchun Sun, Kevin Zhu, W Zheng, Hua Xu

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

16 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
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  4. Review
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  7. Integrating molecular networks with genetic variant interpretation for precision medicine.Wiley interdisciplinary reviews. Systems biology and medicine · 2019
    Review
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  16. Colorectal cancer drug target prediction using ontology-based inference and network analysis.Database : the journal of biological databases and curation · 2015
    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

4 authors at 1 institution in 1 country.

Jingchun Sun
Kevin Zhu
W Zheng
Hua Xu
The University of Texas Health Science Center at Houston · US

Funding

Bridging Genomics and Medicine by Ontology FingerprintsR56LM010680 · NLM · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI LAWSON, ANDREW B. · 2012 to 2013
$499k
NLM NIH HHS 1R56 LM010680
6 · The paper itself

Abstract

backgroundDisease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear.

resultsIn this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes.

conclusionsThe study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

Indexed as

Genome-Wide Association StudyCluster AnalysisComputational BiologyDiseaseGene Expression RegulationHumansPharmaceutical PreparationsProtein Interaction MapsProteinsPharmaceutical PreparationsProteins

Identifiers

PMID25861037
PMCPMC4402590
OpenAlexW1881273775

What OpenQuestion holds

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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.