Evidence map›Paper›PMID 26459019›Full record

ArticleScientific reports2015

Determining Associations between Human Diseases and non-coding RNAs with Critical Roles in Network Control.

Haruna Kagami, Tatsuya Akutsu, Shingo Maegawa, Hiroshi Hosokawa, Jose C Nacher

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
  2. Article
  3. Measuring criticality in control of complex biological networks.NPJ systems biology and applications · 2024
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  8. 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

5 authors.

Haruna KagamiDepartment 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.
Shingo MaegawaDepartment of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.
Hiroshi HosokawaDepartment of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.
Jose C NacherDepartment of Information Science, Faculty of Science, Toho University, Funabashi, 274-8510, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deciphering the association between life molecules and human diseases is currently an important task in systems biology. Research over the past decade has unveiled that the human genome is almost entirely transcribed, producing a vast number of non-protein-coding RNAs (ncRNAs) with potential regulatory functions. More recent findings suggest that many diseases may not be exclusively linked to mutations in protein-coding genes. The combination of these arguments poses the question of whether ncRNAs that play a critical role in network control are also enriched with disease-associated ncRNAs. To address this question, we mapped the available annotated information of more than 350 human disorders to the largest collection of human ncRNA-protein interactions, which define a bipartite network of almost 93,000 interactions. Using a novel algorithmic-based controllability framework applied to the constructed bipartite network, we found that ncRNAs engaged in critical network control are also statistically linked to human disorders (P-value of P = 9.8 × 10(-109)). Taken together, these findings suggest that the addition of those genes that encode optimized subsets of ncRNAs engaged in critical control within the pool of candidate genes could aid disease gene prioritization studies.

Indexed as

Gene Expression RegulationGene Regulatory NetworksGenetic Association StudiesGenetic Predisposition to DiseaseHumansModels, BiologicalProtein BindingRNA-Binding ProteinsRNA, UntranslatedRNA-Binding ProteinsRNA, Untranslated

Identifiers

PMID26459019
PMCPMC4602215

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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.