Evidence map›Paper›PMID 27168792›Full record

ArticleExperimental and therapeutic medicine2016

Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm.

Hong Yue, B O Yang, Fang Yang, Xiao-Li Hu, Fan-Bin Kong

Open access · diamondAbstract read
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Article in Experimental and therapeutic medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 24% 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

4 citing papers in PubMed, 8 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

5 authors at 1 institution in 1 country.

Hong YueDepartment of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
B O YangDepartment of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
Fang YangDepartment of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
Xiao-Li HuDepartment of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
Fan-Bin KongDepartment of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
People’s Hospital of Rizhao · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis.

Indexed as

Alzheimer's diseasedifferentially co-expressed genes and linksempirical Bayesiangene co-expression analysissearch tool for the retrieval of interacting genes/proteins databaseweighted gene co-expression network analysis

Identifiers

PMID27168792
PMCPMC4840697
OpenAlexW2288651021

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.