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Record W2416707841 · doi:10.1007/978-1-62703-730-3_3

Design and Evaluation of Clinically Relevant SOFA-HDV Ribozymes Targeting HIV RNA

2013· article· en· W2416707841 on OpenAlexaff
Robert J. Scarborough, Michel V. Lévesque, Jean‐Pierre Perreault, Anne Gatignol

Bibliographic record

VenueMethods in molecular biology · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversité de SherbrookeMcGill University
Fundersnot available
KeywordsRibozymeRNAComputational biologyBiologyNucleic acidLigase ribozymeVirologyHuman immunodeficiency virus (HIV)GeneticsGene

Abstract

fetched live from OpenAlex

Nucleic acid therapies targeting HIV replication have the potential to be used in conjunction with or in place of the standard small-molecule therapies. Among the different classes of nucleic acid therapies, several ribozymes (Rzs, RNA enzymes) have been developed to target HIV RNA. The design of Rzs targeting HIV RNA is complicated by the sequence diversity of viral strains and the structural diversity of their target sites. Using the SOFA-HDV Rz as an example, this chapter describes methods that can be used to design Rzs for controlling HIV replication. We describe how to (1) identify highly conserved Rz target sites in HIV RNA; (2) generate a set of Rzs with the potential to be used as therapeutics; and (3) screen these Rzs for activity against HIV production.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.438
Teacher spread0.379 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations16
Published2013
Admission routes1
Has abstractyes

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