Evidence map›Paper›PMID 30867695›Full record

ArticleExperimental and therapeutic medicine2019

Predicting the target genes of miRNAs in preterm via targetscore algorithm.

Kexin Lu, Junzhi Huang, Yandong Yang, Dongli Lu

Abstract read
In one paragraph

Article in Experimental and therapeutic medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Kexin LuDepartment of Obstetrics and Gynecology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, P.R. China.
Junzhi HuangDepartment of Obstetrics and Gynecology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, P.R. China.
Yandong YangDepartment of Obstetrics and Gynecology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, P.R. China.
Dongli LuDepartment of Neurology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compared with normal neonates, preterm infants have an immature immune system which causes them to have a higher morbidity rate and even death. In order to reduce the mortality of newborns, we need to find the target genes which affect the preterm and understand their mechanism. It has been verified that microRNA (miRNA)-200 and miRNA-182 are closely related to the incidence of preterm. Therefore, it is significant to predict the target genes which are regulated by them for further understanding the mechanism of preterm. We chose the targetscore method for calculating the variational Bayesian-Gaussian mixture model (VB-GMM) as the target genes prediction method. It is designed for condition-specific target predictions and not limited to predict conserved genes, so the results are more accurate than previous sequence-based target prediction algorithms. In this study, our major contribution is to predict the target mRNAs of the chosen miRNAs with the gene expression profiles and a new method, which can effectively improve the accuracy of the prediction.

Indexed as

bioinformaticsmicroRNApretermtargetscorevariational Bayesian-Gaussian mixture model

Identifiers

PMID30867695
PMCPMC6396006

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.