Evidence map›Paper›PMID 25014374›Full record

ArticleIET systems biology2014

Construction and analysis of microRNA-transcription factor regulation network in Arabidopsis.

Lie Tang, Zhao Zhang, Peizhen Gu, Ming Chen

Open access · greenAbstract read
In one paragraph

Article in IET systems biology, 2014. 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, top 90% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

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 at 1 institution in 1 country.

Lie TangDepartment of Applied Bioscience, College of Agronomy and Biotechnology, Hangzhou 310058, People's Republic of China. mchen@zju.edu.cn.
Zhao ZhangDepartment of Bioinformatics, College of Life Sciences, Hangzhou, Zhejiang, People's Republic China.
Peizhen GuDepartment of Control Science and Engineering, Zhejiang University, Hangzhou 310058, People's Republic of China.
Ming ChenDepartment of Bioinformatics, College of Life Sciences, Hangzhou, Zhejiang, People's Republic China.
Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcription factors (TFs) and microRNAs (miRNAs) are two major types of regulators of gene expression, at transcriptional and post-transcriptional levels, respectively. By gathering their gene regulatory relationships, gene regulatory networks (GRNs) could be formed. A network motif is a type of connection pattern among a set of nodes which appears significantly more frequently than in random networks. Investigations of the network motifs often yield biological insights into the nature of the network. The previous study on miRNA-TF regulation networks concentrated on animals, and relied heavily on computational predictions. The authors collected data concerning miRNA regulation and transcriptional regulation relationships in Arabidopsis from publicly available databases, and further incorporated them with the protein-protein interaction data. All the data in the author's collection are supported by experiments. They screened the network motifs, whose size ranges between 1 and 4. The biological implications of the motifs were further analysed, and a flower development related network was constructed as an example. In this example, they illustrated the relevance of the network with the given process, and proposed the association of several genes with flowers by a network cluster identification. In this study, they analysed the properties of the GRN in Arabidopsis, and discussed their biological implications, as well as their potential applications.

Indexed as

Gene Expression ProfilingGene Expression Regulation, PlantAlgorithmsArabidopsisCluster AnalysisComputational BiologyComputer SimulationDatabases, FactualDatabases, GeneticFlowersGene Expression RegulationGene Regulatory NetworksMicroRNAsProtein Interaction MappingSoftwareSpecies SpecificityMicroRNAsTranscription Factors

Identifiers

PMID25014374
PMCPMC8687243
OpenAlexW2122503570

What OpenQuestion holds

Textmetadata
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.