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Record W3121721028 · doi:10.1016/j.ymthe.2021.01.018

Circular RNAs: Expression, localization, and therapeutic potentials

2021· review· en· W3121721028 on OpenAlexafffund
Qiwei Yang, Feiya Li, Alina T. He, Burton B. Yang

Bibliographic record

VenueMolecular Therapy · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsSunnybrook Health Science CentreSunnybrook HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchChina Scholarship Council
KeywordsExpression (computer science)Cell biologyComputational biologyBiologyComputer science

Abstract

fetched live from OpenAlex

Circular RNAs (circRNAs) are RNAs with a unique circular structure that is generated from back-splicing processes. These circular molecules were discovered more than 40 years ago but failed to raise scientific interest until lately. Increasing studies have found that these circular RNAs might not just be byproducts of the splicing process but possess important regulatory functions through different cellular events. Most circular RNAs are currently being studied in the field of cancer, and many of them have been confirmed to be involved in the process of tumorigenesis. However, many circular RNAs are implicated in the developmental stages of diseases other than cancer. In this review, we focus on discussing the role of circular RNAs in non-cancer diseases, especially in cardiovascular diseases. Following the summary of the life cycle of circRNAs, we provide input on studying circRNA-protein interactions based on our experience, which modulate protein translocation. Furthermore, we outline the potential of circRNAs to be potent biomarkers, effective therapeutic targets, and potential treatments in cardiovascular diseases as well as other non-cancer fields.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.319
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations170
Published2021
Admission routes2
Has abstractno

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