Evidence map›Paper›PMID 33725751›Full record

ArticleJournal of integrative bioinformatics2021

Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection.

Yılmaz Mehmet Demirci, Müşerref Duygu Saçar Demirci

Open access · diamondAbstract read
In one paragraph

Article in Journal of integrative bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

18 citing papers in PubMed, 24 citations in OpenAlex.

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  13. Therapeutic prospects of ceRNAs in COVID-19.Frontiers in cellular and infection microbiology · 2022
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  16. Hsa_circ_0000479/Hsa-miR-149-5p/RIG-I, IL-6 Axis: A Potential Novel Pathway to Regulate Immune Response against COVID-19.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2022
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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

2 authors at 1 institution in 1 country.

Yılmaz Mehmet DemirciFaculty of Engineering, Engineering Science Department, Abdullah Gül University, 38080Kayseri, Turkey.
Müşerref Duygu Saçar DemirciFaculty of Life and Natural Sciences, Bioinformatics Department, Abdullah Gül University, 38080Kayseri, Turkey.
Abdullah Gül University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.

Indexed as

COVID-19HumansMicroRNAsRNA, CircularRNA, MessengerSARS-CoV-2MicroRNAsRNA, CircularRNA, MessengercircRNAgene regulationmachine learningmiRNASARS-CoV-2

Identifiers

PMID33725751
PMCPMC8035959
OpenAlexW3136242952

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.