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Record W3207571434 · doi:10.1080/15476286.2021.1989201

Alu RNA and their roles in human disease states

2021· article· en· W3207571434 on OpenAlex
Daniel Gussakovsky, Sean A. McKenna

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRNA Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsAlu elementRNABiologyIntronGeneticsNon-coding RNARNA editingTranscription (linguistics)Human genomeSmall nuclear RNAGenomeComputational biologyGene

Abstract

fetched live from OpenAlex

Alu RNA are implicated in the poor prognosis of several human disease states. These RNA are transcription products of primate specific transposable elements called Alu elements. These elements are extremely abundant, comprising over 10% of the human genome, and 100 to 1000 cytoplasmic copies of Alu RNA per cell. Alu RNA do not have a single universal functional role aside from selfish self-propagation. Despite this, Alu RNA have been found to operate in a diverse set of translational and transcriptional mechanisms. This review will focus on the current knowledge of Alu RNA involved in human disease states and known mechanisms of action. Examples of Alu RNA that are transcribed in a variety of contexts such as introns, mature mRNA, and non-coding transcripts will be discussed. Past and present challenges in studying Alu RNA, and the future directions of Alu RNA in basic and clinical research will also be examined.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.248
Teacher spread0.238 · 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