Evidence map›Paper›PMID 34241565›Full record

ArticleRNA biology2021

Structure-aware machine learning identifies microRNAs operating as Toll-like receptor 7/8 ligands.

Martin Raden, Thomas Wallach, Milad Miladi, Yuanyuan Zhai, Christina Krüger, Zoé J Mossmann, Paul Dembny, Rolf Backofen, Seija Lehnardt

Open access · bronzeAbstract read
In one paragraph

Article in RNA biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.6field-weighted citation impact, top 40% 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

11 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. miR-574-5p in epigenetic regulation and Toll-like receptor signaling.Cell communication and signaling : CCS · 2024
    Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. 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

9 authors at 2 institutions in 1 country.

Martin RadenBioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.ORCID 0000-0002-7926-5911
Thomas WallachInstitute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Milad MiladiBioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.ORCID 0000-0002-0173-3009
Yuanyuan ZhaiInstitute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Christina KrügerInstitute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Zoé J MossmannInstitute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Paul DembnyInstitute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Rolf BackofenBioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.ORCID 0000-0001-8231-3323
Seija LehnardtInstitute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany.ORCID 0000-0002-1068-7112
Humboldt-Universität zu Berlin · DEUniversity of Freiburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) can serve as activation signals for membrane receptors, a recently discovered function that is independent of the miRNAs' conventional role in post-transcriptional gene regulation. Here, we introduce a machine learning approach, BrainDead, to identify oligonucleotides that act as ligands for single-stranded RNA-detecting Toll-like receptors (TLR)7/8, thereby triggering an immune response. BrainDead was trained on activation data obtained from

Indexed as

Gene Expression RegulationMachine LearningAnimalsHumansLigandsMiceMice, Inbred C57BLMicrogliaMicroRNAsOligonucleotidesToll-Like Receptor 7Toll-Like Receptor 8LigandsMicroRNAsOligonucleotidesToll-Like Receptor 7Toll-Like Receptor 8ligandmachine learningmiRNARNA structureTLR

Identifiers

PMID34241565
PMCPMC8677043
OpenAlexW3182011789

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.