MétaCan
Menu
Back to cohort
Record W2407571998 · doi:10.1007/978-1-61779-117-8_12

Impact of MicroRNA in Normal and Pathological Respiratory Epithelia

2011· article· en· W2407571998 on OpenAlexaff
Lisa Giovannini‐Chami, Nathalie Grandvaux, Laure‐Emmanuelle Zaragosi, Karine Robbe-Sermesant, Brice Marcet, Bruno Cardinaud, Christelle Coraux, Yves Berthiaume, Rainer Waldmann, Bernard Mari, Pascal Barbry

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsHôtel-Dieu de MontréalHôpital Saint-LucCentre Hospitalier de l’Université de Montréal
FundersFP7 HealthCanceropôle PACACentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsmicroRNAMulticellular organismBiologyComputational biologyCystic fibrosisIdentification (biology)BioinformaticsGeneGenetics

Abstract

fetched live from OpenAlex

Extensive sequencing efforts, combined with ad hoc bioinformatics developments, have now led to the identification of 1222 distinct miRNAs in human (derived from 1368 distinct genomic loci) and of many miRNAs in other multicellular organisms. The present chapter is aimed at describing a general experimental strategy to identify specific miRNA expression profiles and to highlight the functional networks operating between them and their mRNA targets, including several miRNAs deregulated in cystic fibrosis and during differentiation of airway epithelial cells.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.377
Teacher spread0.345 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMethods in molecular biologySame topicMicroRNA in disease regulationFrench-language works237,207