Evidence map›Paper›PMID 33680358›Full record

ReviewComputational and structural biotechnology journal2021

Integrative approaches for analysis of mRNA and microRNA high-throughput data.

Petr V Nazarov, Stephanie Kreis

Abstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. MiRNA Dysregulation in Brain Injury: AnCurrent neuropharmacology · 2025
    Article
  4. Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAs.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Transcriptomic Analysis of Human PodocytesKidney international reports · 2023
    Article
  14. Article
  15. Article
  16. Non-Coding RNA in Penile Cancer.Frontiers in oncology · 2022
    Review
  17. Review
  18. Article
  19. Review
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.

Petr V NazarovMultiomics Data Science Research Group, Department of Oncology & Quantitative Biology Unit, Luxembourg Institute of Health (LIH), Strassen L-1445, Luxembourg.
Stephanie KreisSignal Transduction Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux L-4367, Luxembourg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species.

Indexed as

CCA, canonical correlation analysisCDS, coding sequencecircRNA, circular RNACLASH, cross-linking, ligation and sequencing of hybridsCLIP, cross-linking immunoprecipitationCNN, convolutional neural networkData integrationGO, gene ontologyICA, independent component analysislncRNA, long non-coding RNAMatrix factorizationmicroRNAmiRNA, microRNAmRNA, messenger RNANGS, next-generation sequencingNMF, non-negative matrix factorizationPCA, principal component analysisRNASeq, high-throughput RNA sequencingTarget predictionTDMD, target RNA-directed miRNA degradationTF, transcription factorsTranscriptomics

Identifiers

PMID33680358
PMCPMC7895676

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.