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Record W2006992053 · doi:10.3389/fgene.2015.00087

Non-coding RNA in neural function, disease, and aging

2015· review· en· W2006992053 on OpenAlexafffund
Kirk Szafranski

Bibliographic record

VenueFrontiers in Genetics · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsRNAmicroRNABiologyNon-coding RNALong non-coding RNAComputational biologySmall nucleolar RNAFunction (biology)GeneticsGeneNeuroscience

Abstract

fetched live from OpenAlex

Declining brain and neurobiological function is arguably one of the most common features of human aging. The study of conserved aging processes as well as the characterization of various neurodegenerative diseases using different genetic models such as yeast, fly, mouse, and human systems is uncovering links to non-coding RNAs. These links implicate a variety of RNA-regulatory processes, including microRNA function, paraspeckle formation, RNA-DNA hybrid regulation, nucleolar RNAs and toxic RNA clearance, amongst others. Here we highlight these connections and reveal over-arching themes or questions related to recently appreciated roles of non-coding RNA in neural function and dysfunction across lifespan.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.306
Teacher spread0.282 · 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 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

Citations84
Published2015
Admission routes2
Has abstractyes

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