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Record W1481377573 · doi:10.1007/978-1-60327-547-7_18

Protein Components of the microRNA Pathway and Human Diseases

2008· review· en· W1481377573 on OpenAlexfundno aff
Marjorie Perron, Patrick Provost

Bibliographic record

VenueMethods in molecular biology · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsDroshaDicerArgonauteBiologymicroRNARNA-binding proteinTranslation (biology)GeneticsBiogenesisMessenger RNARNA interferenceCell biologyRNAGene

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) are key regulators of messenger RNA (mRNA) translation known to be involved in a wide variety of cellular processes. In fact, their individual importance is reflected in the diseases that may arise upon the loss, mutation or dysfunction of specific miRNAs. It has been appreciated only recently that diseases may also develop when the protein components of the miRNA machinery itself are affected. The core enzymes of the major protein complexes involved in miRNA biogenesis and function, such as the ribonucleases III (RNases III) Drosha and Dicer as well as Argonaute 2 (Ago2), appear to be essential. However, the accessory proteins of the miRNA pathway, such as the DiGeorge syndrome critical region gene 8 (DGCR8) protein, Exportin-5 (Exp-5), TAR RNA binding protein (TRBP) and fragile X mental retardation protein (FMRP), are each related, in various ways, to specific genetic diseases.

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 score0.951
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 science0.0000.001
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 head0.036
GPT teacher head0.370
Teacher spread0.334 · 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

Citations67
Published2008
Admission routes1
Has abstractyes

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