Evidence map›Paper›PMID 21912507›Full record

ArticleInternational journal of biological sciences2011

Identification of candidate genes related to bovine marbling using protein-protein interaction networks.

Dajeong Lim, Nam-Kuk Kim, Hye-Sun Park, Seung-Hwan Lee, Yong-Min Cho, Sung Jong Oh, Tae-Hun Kim, Heebal Kim

Abstract read
In one paragraph

Article in International journal of biological sciences, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

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  6. Analyzing host-viral interactome of SARS-CoV-2 for identifying vulnerable host proteins during COVID-19 pathogenesis.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2021
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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.

Dajeong LimDivision of Animal Genomics and Bioinformatics, National Institute of Animal Science, Rural Development Administration, Suwon, Republic of Korea.
Nam-Kuk Kim
Hye-Sun Park
Seung-Hwan Lee
Yong-Min Cho
Sung Jong Oh
Tae-Hun Kim
Heebal Kim

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The present study systemically analyzed genes associated with bovine marbling score and identified their relationships. The candidate nodes were obtained using MedScan text-mining tools and linked by protein-protein interaction (PPI) from the Human Protein Reference Database (HPRD). To determine key node of marbling, the degree and betweenness centrality (BC) were used. The hub nodes and biological pathways of our network are consistent with the previous reports about marbling traits, and also suggest unknown candidate genes associated with intramuscular fat. Five nodes were identified as hub genes, which was consistent with the network analysis using quantitative reverse-transcription PCR (qRT-PCR). Key nodes of the PPI network have positive roles (PPARγ, C/EBPα, and RUNX1T1) and negative roles (RXRA, CAMK2A) in the development of intramuscular fat by several adipogenesis-related pathways. This study provides genetic information for identifying candidate genes for the marbling trait in bovine.

Indexed as

Protein Interaction MapsQuantitative Trait LociAdiposityAnimalsCattlePhenotypeRegression AnalysisBovineHanwooMarblingNetworkProtein-Protein Interaction (PPI)

Identifiers

PMID21912507
PMCPMC3164149

What OpenQuestion holds

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Registered trials

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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.