Evidence map›Paper›PMID 25061069›Full record

ArticleBioinformatics (Oxford, England)2014

Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data.

Junpeng Zhang, Thuc Duy Le, Lin Liu, Bing Liu, Jianfeng He, Gregory J Goodall, Jiuyong Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025
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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.

Junpeng ZhangFaculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Children's Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.
Thuc Duy LeFaculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Children's Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.
Lin LiuFaculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Children's Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.
Bing LiuFaculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Children's Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.
Jianfeng HeFaculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Children's Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.
Gregory J GoodallFaculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Children's Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.
Jiuyong LiFaculty of Engineering, Dali University, Dali, Yunnan 671003, China, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia, Children's Cancer Institute Australia, Randwick, NSW 2301, Australia, Kunming University of Science and Technology, Kunming, Yunnan 650500, China and Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationMicroRNAs (miRNAs) play crucial roles in complex cellular networks by binding to the messenger RNAs (mRNAs) of protein coding genes. It has been found that miRNA regulation is often condition-specific. A number of computational approaches have been developed to identify miRNA activity specific to a condition of interest using gene expression data. However, most of the methods only use the data in a single condition, and thus, the activity discovered may not be unique to the condition of interest. Additionally, these methods are based on statistical associations between the gene expression levels of miRNAs and mRNAs, so they may not be able to reveal real gene regulatory relationships, which are causal relationships.

resultsWe propose a novel method to infer condition-specific miRNA activity by considering (i) the difference between the regulatory behavior that an miRNA has in the condition of interest and its behavior in the other conditions; (ii) the causal semantics of miRNA-mRNA relationships. The method is applied to the epithelial-mesenchymal transition (EMT) and multi-class cancer (MCC) datasets. The validation by the results of transfection experiments shows that our approach is effective in discovering significant miRNA-mRNA interactions. Functional and pathway analysis and literature validation indicate that the identified active miRNAs are closely associated with the specific biological processes, diseases and pathways. More detailed analysis of the activity of the active miRNAs implies that some active miRNAs show different regulation types in different conditions, but some have the same regulation types and their activity only differs in different conditions in the strengths of regulation. AVAILABILITY AND IMPLEMENTATION: The R and Matlab scripts are in the Supplementary materials.

Indexed as

Epithelial-Mesenchymal TransitionGene Expression ProfilingGene Regulatory NetworksHumansMicroRNAsNeoplasmsRNA, MessengerMicroRNAsRNA, Messenger

Identifiers

PMID25061069
PMCPMC4609012

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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.