Evidence map›Paper›PMID 28117714›Full record

ArticleGenes2017

Tissue Non-Specific Genes and Pathways Associated with Diabetes: An Expression Meta-Analysis.

Hao Mei, Lianna Li, Shijian Liu, Fan Jiang, Michael Griswold, Thomas Mosley

Open access · goldAbstract read
In one paragraph

Article in Genes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

5 citing papers in PubMed, 13 citations in OpenAlex.

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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

6 authors at 4 institutions in 2 countries.

Hao MeiDepartment of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS 39216, USA. hmei@umc.edu.
Lianna LiDepartment of Biology, Tougaloo College, Jackson, MI 39216, USA. lli@tougaloo.edu.
Shijian LiuShanghai Children's Medical Center, School of Public Health and School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China. arrow64@163.com.
Fan JiangShanghai Children's Medical Center, School of Public Health and School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China. jiangfan@scmc.com.cn.
Michael GriswoldDepartment of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS 39216, USA. mgriswold@umc.edu.
Thomas MosleyDepartment of Neurology, University of Mississippi Medical Center, Jackson, MS 39216, USA. tmosley@umc.edu.
Shanghai Children's Medical Center · CNUniversity of Mississippi Medical Center · USJackson Memorial Hospital · USTougaloo College · US

Funding

Training and Mentoring Core P20GM103476 · NIGMS · UNIVERSITY OF SOUTHERN MISSISSIPPI · PI MICHAEL R GARRETT · 2012 to 2026
$60.2M
NIGMS NIH HHS P20 GM103476
6 · The paper itself

Abstract

We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed that

Indexed as

diabetesgene expressionpathway

Identifiers

PMID28117714
PMCPMC5295038
OpenAlexW2580080859

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.