Evidence map›Paper›PMID 26472186›Full record

ArticleOncotarget2015

Subpathway-GMir: identifying miRNA-mediated metabolic subpathways by integrating condition-specific genes, microRNAs, and pathway topologies.

Li Feng, Yanjun Xu, Yunpeng Zhang, Zeguo Sun, Junwei Han, Chunlong Zhang, Haixiu Yang, Desi Shang, Fei Su, Xinrui Shi and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncotarget, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

13 authors.

Li FengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yanjun XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yunpeng ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Zeguo SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Junwei HanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Chunlong ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Haixiu YangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Desi ShangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Fei SuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Xinrui ShiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Shang LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Chunquan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) regulate disease-relevant metabolic pathways. However, most current pathway identification methods fail to consider miRNAs in addition to genes when analyzing pathways. We developed a powerful method called Subpathway-GMir to construct miRNA-regulated metabolic pathways and to identify miRNA-mediated subpathways by considering condition-specific genes, miRNAs, and pathway topologies. We used Subpathway-GMir to analyze two liver hepatocellular carcinomas (LIHC), one stomach adenocarcinoma (STAD), and one type 2 diabetes (T2D) data sets. Results indicate that Subpathway-GMir is more effective in identifying phenotype-associated metabolic pathways than other methods and our results are reproducible and robust. Subpathway-GMir provides a flexible platform for identifying abnormal metabolic subpathways mediated by miRNAs, and may help to clarify the roles that miRNAs play in a variety of diseases. The Subpathway-GMir method has been implemented as a freely available R package.

Indexed as

AnimalsHumansMetabolic Networks and PathwaysMicroRNAsModels, GeneticRNA, MessengerMicroRNAsRNA, MessengercancermicroRNAmRNApathway identificationregulation

Identifiers

PMID26472186
PMCPMC4770763

What OpenQuestion holds

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LicenceCC BY
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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.