Evidence map›Paper›PMID 28729650›Full record

ArticleScientific reports2017

Large-Scale Analysis of Drug Side Effects via Complex Regulatory Modules Composed of microRNAs, Transcription Factors and Gene Sets.

Xiaodong Jia, Qing Jin, Xiangqiong Liu, Xiusen Bian, Yunfeng Wang, Lei Liu, Hongzhe Ma, Fujian Tan, Mingliang Gu, Xiujie Chen

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2017. 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
0.2field-weighted citation impact, top 44% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

10 authors at 3 institutions in 1 country.

Xiaodong JiaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Qing JinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xiangqiong LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xiusen BianCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yunfeng WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Lei LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Hongzhe MaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Fujian TanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Mingliang GuJoint Laboratory for Translational Medicine Research, Beijing Institute of Genomics, Chinese Academy of Sciences & Liaocheng People's Hospital, Liaocheng, China. guml@big.ac.cn.
Xiujie ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. chenxiujie@ems.hrbmu.edu.cn.
Harbin Medical University · CNChinese Academy of Sciences · CNLiaocheng People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying the occurrence mechanism of drug-induced side effects (SEs) is critical for design of drug target and new drug development. The expression of genes in biological processes is regulated by transcription factors(TFs) and/or microRNAs. Most of previous studies were focused on a single level of gene or gene sets, while studies about regulatory relationships of TFs, miRNAs and biological processes are very rare. Discovering the complex regulating relations among TFs, gene sets and miRNAs will be helpful for researchers to get a more comprehensive understanding about the mechanism of side reaction. In this study, a framework was proposed to construct the relationship network of gene sets, miRNAs and TFs involved in side effects. Through the construction of this network, the potential complex regulatory relationship in the occurrence process of the side effects was reproduced. The SE-gene set network was employed to characterize the significant regulatory SE-gene set interaction and molecular basis of accompanied side effects. A total of 117 side effects complex modules including four types of regulating patterns were obtained from the SE-gene sets-miRNA/TF complex regulatory network. In addition, two cases were used to validate the complex regulatory modules which could more comprehensively interpret occurrence mechanism of side effects.

Indexed as

Gene Regulatory NetworksDrug-Related Side Effects and Adverse ReactionsGene Expression RegulationHumansMicroRNAsNeutropeniaPneumoniaTranscription FactorsMicroRNAsTranscription Factors

Identifiers

PMID28729650
PMCPMC5519677
OpenAlexW2735718042

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.