Evidence map›Paper›PMID 34576052›Full record

ArticleInternational journal of molecular sciences2021

Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles.

Eimi Yamaguchi, Tatsuya Akutsu, Jose C Nacher

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2021. 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
0.1field-weighted citation impact, top 55% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

3 authors at 2 institutions in 1 country.

Eimi YamaguchiDepartment of Information Science, Faculty of Science, Toho University, Funabashi 274-8510, Japan.
Tatsuya AkutsuBioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan.ORCID 0000-0001-9763-797X
Jose C NacherDepartment of Information Science, Faculty of Science, Toho University, Funabashi 274-8510, Japan.
Toho University · JPKyoto University · JP

Funding

Japan Society for the Promotion of Science 18H04113Japan Society for the Promotion of Science 18K11535
6 · The paper itself

Abstract

Recently, network controllability studies have proposed several frameworks for the control of large complex biological networks using a small number of life molecules. However, age-related changes in the brain have not been investigated from a controllability perspective. In this study, we compiled the gene expression profiles of four normal brain regions from individuals aged 20-99 years and generated dynamic probabilistic protein networks across their lifespan. We developed a new algorithm that efficiently identified critical proteins in probabilistic complex networks, in the context of a minimum dominating set controllability model. The results showed that the identified critical proteins were significantly enriched with well-known ageing genes collected from the GenAge database. In particular, the enrichment observed in replicative and premature senescence biological processes with critical proteins for male samples in the hippocampal region led to the identification of possible new ageing gene candidates.

Indexed as

AdultAgedAged, 80 and overAgingAlgorithmsBrainComputational BiologyDatabases, GeneticFemaleGene Expression RegulationGene Regulatory NetworksHippocampusHumansMaleMiddle AgedProtein Interaction Mapsageing processbraincritical controllabilitygene expressionprobabilistic controllabilityprotein networks

Identifiers

PMID34576052
PMCPMC8465977
OpenAlexW3201263222

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.