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The Principles of MiRNA-Masking Antisense Oligonucleotides Technology

2010· article· en· W34714686 on OpenAlexafffund
Zhiguo Wang

Bibliographic record

VenueMethods in molecular biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersCanadian Institutes of Health Research
KeywordsMasking (illustration)OligonucleotidemicroRNAComputer scienceComputational biologyBiologyGeneticsArtGene

Abstract

fetched live from OpenAlex

MiRNA-masking antisense oligonucleotides technology (miR-mask) is an anti-microRNA antisense oligodeoxyribonucleotide (AMO) approach of a different sort. A standard miR-mask is single-stranded 2'-O-methyl-modified oligoribonucleotide (or other chemically modified) that is a 22-nt antisense to a protein-coding mRNA as a target for an endogenous miRNA of interest. Instead of binding to the target miRNA like an AMO, an miR-mask does not directly interact with its target miRNA but binds to the binding site of that miRNA in the 3' UTR of the target mRNA by fully complementary mechanism. In this way, the miR-mask covers up the access of its target miRNA to the binding site so as to derepress its target gene (mRNA) via blocking the action of its target miRNA. The anti-miRNA action of an miR-mask is gene-specific because it is designed to be fully complementary to the target mRNA sequence of an miRNA. The anti-miRNA action of an miR-mask is miRNA-specific as well because it is designed to target the binding site of that particular miRNA. The miR-mask approach is a valuable supplement to the AMO technique; while AMO is indispensable for studying the overall function of an miRNA, the miR-mask might be more appropriate for studying the specific outcome of regulation of the target gene by the miRNA. This technology was first established by my research group in 2007 (Xiao et al., J Cell Physiol 212:285-292; Wang et al., J Mol Med 86:772-783, 2008) and a similar approach with the same concept was subsequently reported by Schier's laboratory (Choi et al., Science 318:271-274, 2007).

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.003

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 head0.016
GPT teacher head0.364
Teacher spread0.348 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations116
Published2010
Admission routes2
Has abstractyes

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