Evidence map›Paper›PMID 41006567›Full record

ArticleScientific reports2025

Utilizing partial information decomposition to evaluate the complex interplay between microRNA and RNA-binding protein in regulating the shared target mRNA.

Tianwu Zhang, Yanlin Chen, Wenrong Wang, Chu Pan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Tianwu ZhangSchool of Software, Henan University of Engineering, Zhengzhou, Henan, China.
Yanlin ChenSchool of Software, Henan University of Engineering, Zhengzhou, Henan, China.
Wenrong WangDepartment of Clinical Medicine, Zhengzhou University, Zhengzhou, Henan, China.
Chu PanCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China. chu.pan@hnu.edu.cn.

Funding

Key Scientific Research Project of Higher Education Institutions in Henan Province 23A520049National Natural Science Foundation of China 62102144
6 · The paper itself

Abstract

MicroRNAs (miRNAs) bind to the 3' untranslated region of mRNA transcripts, exerting inhibitory activity over gene expression. RNA-binding proteins (RBPs) involved in post-transcriptional regulation also play a pivotal role in modulating mRNA, exhibiting similar binding and regulatory effects to mRNA as miRNAs. This convergence raises the intriguing possibility of coordinated or competitive regulation between miRNAs and RBPs when targeting the common mRNA. However, accurately quantifying the complex regulatory relationship between miRNAs and RBPs remains a challenge. To address this challenge, we here propose a novel multivariate information-based approach to quantitatively capture the nonlinear regulatory relationships between miRNAs and RBPs in the context of shared mRNA targets. Our method integrates the sequence information with gene expression data, unveiling a comprehensive perspective on such an intricate regulatory network. Our findings reveal a prevalent synergistic relationship between miRNAs and RBPs, surpassing instances of competitive relationship. This innovative approach enhances our understanding of the complex interplay between miRNAs and RBPs, shedding light on the cooperative mechanisms that drive post-transcriptional regulation.

Indexed as

Gene Expression RegulationMicroRNAsRNA-Binding ProteinsRNA, Messenger3' Untranslated RegionsComputational BiologyGene Regulatory NetworksHumans3' Untranslated RegionsMicroRNAsRNA-Binding ProteinsRNA, MessengerCooperative and competitive regulatory relationshipmicroRNAsPartial information decompositionRNA-binding proteins

Identifiers

PMID41006567
PMCPMC12474933

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.