Evidence map›Paper›PMID 31380422›Full record

ArticleBioMed research international2019

The Model of Aging Acceleration Network Reveals the Correlation of Alzheimer's Disease and Aging at System Level.

Mengyu Zhou, Xiaoqiong Xia, Hao Yan, Sijia Li, Shiyu Bian, Xianzheng Sha, Yin Wang

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2019. 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
0.6field-weighted citation impact, top 33% 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, 9 citations in OpenAlex.

  1. Review
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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

7 authors at 1 institution in 1 country.

Mengyu ZhouDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110122, Liaoning Province, China.
Xiaoqiong XiaDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110122, Liaoning Province, China.
Hao YanDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110122, Liaoning Province, China.
Sijia LiDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110122, Liaoning Province, China.
Shiyu BianChina Medical University-The Queen's University of Belfast Joint College, China Medical University, Shenyang 110122, Liaoning Province, China.
Xianzheng ShaDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110122, Liaoning Province, China.ORCID https://orcid.org/0000-0002-1083-7862
Yin WangDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110122, Liaoning Province, China.ORCID https://orcid.org/0000-0003-3120-8579
China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the incidence of senile dementia continues to increase, researches on Alzheimer's disease (AD) have become more and more important. Several studies have reported that there is a close relationship between AD and aging. Some researchers even pointed out that if we wanted to understand AD in depth, mechanisms of AD based on accelerated aging must be studied. Nowadays, machine learning techniques have been utilized to deal with large and complex profiles, thus playing an important role in disease researches (i.e., modelling biological systems, identifying key modules based on biological networks, and so on). Here, we developed an aging predictor and an AD predictor using machine learning techniques, respectively. Both aging and AD biomarkers were identified to provide insights into genes associated with AD. Besides, aging scores were calculated to reflect the aging process of brain tissues. As a result, the aging acceleration network and the aging-AD bipartite graph were constructed to delve into the relationship between AD and aging. Finally, a series of network and enrichment analyses were also conducted to gain further insights into the mechanisms of AD based on accelerated aging. In a word, our results indicated that aging may contribute to the development of AD by affecting the function of the immune system and the energy metabolism process, where the immune system may play a more prominent role in AD.

Indexed as

Machine LearningAgingAlgorithmsAlzheimer DiseaseBiomarkersBrainHumansSystems BiologyBiomarkers

Identifiers

PMID31380422
PMCPMC6662274
OpenAlexW2957983793

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.