Evidence map›Paper›PMID 30326837›Full record

ArticleBMC bioinformatics2018

Discovering the 'Dark matters' in expression data of miRNA based on the miRNA-mRNA and miRNA-lncRNA networks.

Cong Pian, Guangle Zhang, Sanling Wu, Fei Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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.

Cong PianMinistry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Guangle ZhangDepartment of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China.
Sanling WuAnalysis Center of Agrobiology and Environmental Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Fei LiMinistry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, Hangzhou, 310058, Zhejiang, China. lifei18@zju.edu.cn.

Funding

National Key Research and Development Program 2016YFC1200600The Science and Technology Research Project of the Ministry of Education V201308
6 · The paper itself

Abstract

backgroundSince miRNAs can play important roles in different cancer types, how to discover cancer related miRNAs is an important issue. In general, the miRNAs with differential expression is the focus of attention. However, some important cancer related miRNAs are not excavated by differential expression analysis. We take this type of miRNAs as 'dark matters' (DM-miRNA). It is our great interests to develop an algorithm to discover DM-miRNAs.

resultsAn effective method was developed to find DM-miRNAs. This method is mainly for mining potential DM-miRNAs by building basic miRNA-mRNA network (BMMN) and miRNA-lncRNA network (BMLN). The results indicate that miRNA-mRNA and miRNA-lncRNA interactions can be used as novel cancer biomarkers.

conclusionsThe BMMN and BMLN can excavate the non-differentially expressed miRNAs which play an important role in the cancer. What's more, the edge biomarkers (miRNA-mRNA and miRNA-lncRNA interactions) contain more information than the node biomarkers. It will contribute to developing novel therapeutic candidates in cancers.

Indexed as

Gene Regulatory NetworksHumansMicroRNAsRNA, Long NoncodingMicroRNAsRNA, Long NoncodingBiomarkersDark mattersmiRNA-lncRNA networkmiRNA-mRNA network

Identifiers

PMID30326837
PMCPMC6192342

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