Evidence map›Paper›PMID 40136664›Full record

ArticleCells2025

Integration of Dynamical Network Biomarkers, Control Theory and

Kazutaka Akagi, Ying-Jie Jin, Keiichi Koizumi, Makito Oku, Kaisei Ito, Xun Shen, Jun-Ichi Imura, Kazuyuki Aihara, Shigeru Saito

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cells, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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

9 authors.

Kazutaka AkagiDivision of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan.ORCID 0000-0001-7694-425X
Ying-Jie JinGraduate School of Pharma-Medical Sciences, University of Toyama, Toyama 930-0194, Japan.
Keiichi KoizumiDivision of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan.ORCID 0000-0002-0349-4144
Makito OkuResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan.ORCID 0000-0002-0282-2361
Kaisei ItoDepartment of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan.
Xun ShenGraduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.ORCID 0000-0002-8827-5791
Jun-Ichi ImuraDepartment of Systems and Control Engineering, School of Engineering, Institute of Science Tokyo, Tokyo 152-8552, Japan.
Kazuyuki AiharaInternational Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo 113-0033, Japan.
Shigeru SaitoResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan.ORCID 0000-0002-8940-3708

Funding

Japan Science and Technology Agency JPMJMS2021
6 · The paper itself

Abstract

Metabolic syndrome (MetS) is a subclinical disease, resulting in increased risk of type 2 diabetes (T2D), cardiovascular diseases, cancer, and mortality. Dynamical network biomarkers (DNB) theory has been developed to provide early-warning signals of the disease state during a preclinical stage. To improve the efficiency of DNB analysis for the target genes discovery, the DNB intervention analysis based on the control theory has been proposed. However, its biological validation in a specific disease such as MetS remains unexplored. Herein, we identified eight candidate genes from adipose tissue of MetS model mice at the preclinical stage by the DNB intervention analysis. Using

Indexed as

BiomarkersDEAD-box RNA HelicasesDrosophila melanogasterDrosophila ProteinsMetabolic SyndromeAdipocytesAdipose TissueAnimalsDiet, High-FatDisease Models, AnimalHumansMaleMiceMice, Inbred C57BLBiomarkersDEAD-box RNA HelicasesDrosophila ProteinsDNB intervention analysisDrosophila melanogasterdynamical network biomarkers theorymetabolic syndrome

Identifiers

PMID40136664
PMCPMC11941168

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.