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Record W2572864250 · doi:10.1186/s12985-016-0667-3

The sense behind retroviral anti-sense transcription

2017· article· en· W2572864250 on OpenAlexafffund
Mamneet Manghera, Alycia Magnusson, Renée N. Douville

Bibliographic record

VenueVirology Journal · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyRetrovirusSense (electronics)Long terminal repeatTranscription (linguistics)Endogenous retrovirusVirologyGeneAntisense RNAGeneticsRNAGene expressionGenome

Abstract

fetched live from OpenAlex

Retroviruses are known to rely extensively on the expression of viral proteins from the sense proviral genomic strand. Yet, the production of regulatory retroviral proteins from antisense-encoded viral genes is gaining research attention, due to their clinical significance. This report will discuss what is known about antisense transcription in Retroviridae, and provide new information about antisense transcriptional regulation through a comparison of Human Immunodeficiency Virus (HIV), Human T-cell Lymphotrophic Virus (HTLV-1) and endogenous retrovirus-K (ERVK) long terminal repeats (LTRs). We will attempt to demonstrate that the potential for antisense transcription is more widespread within retroviruses than has been previously appreciated, with this feature being the rule, rather than the exception.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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 teacher head, not a consensus.

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
Published2017
Admission routes2
Has abstractyes

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