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Record W4200227978 · doi:10.1089/nat.2021.0085

Challenges and Opportunities for Nucleic Acid Therapeutics

2021· article· en· W4200227978 on OpenAlexaff
David R. Corey, Masad J. Damha, Muthiah Manoharan

Bibliographic record

VenueNucleic Acid Therapeutics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsMcGill University
FundersNational Institute of General Medical Sciences
KeywordsNucleic acidComputational biologyOligonucleotideRNARisk analysis (engineering)BiologyComputer scienceMedicineDNAGeneticsGene

Abstract

fetched live from OpenAlex

After decades overcoming difficult problems, antisense oligonucleotide (ASO), duplex RNA (siRNA), and messenger RNA (mRNA) nucleic acid therapeutic strategies are finally demonstrating clinical benefits. This success presents new challenges. What goals remain for basic research? Will there be an explosion of clinical applications that benefit many patients with different diseases, or will success be restricted to diseases that are ideal for the application of current technologies? The aim of this perspective is to describe a selection of the major goals for the next decade.

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)0.013
metaresearch head score (Gemma)0.006
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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.010
Scholarly communication0.0070.016
Open science0.0020.007
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0130.005

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 head0.109
GPT teacher head0.295
Teacher spread0.187 · 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

Citations73
Published2021
Admission routes1
Has abstractyes

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