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Record W2048401962 · doi:10.1155/2012/893048

BRASERO: A Resource for Benchmarking RNA Secondary Structure Comparison Algorithms

2012· article· en· W2048401962 on OpenAlexafffund
Julien Allali, Cédric Saule, Cédric Chauve, Yves d’Aubenton-Carafa, Alain Denise, Christine Drevet, Pascal Ferraro, Daniel Gautheret, Claire Herrbach, Fabrice Leclerc, Antoine de Monte, Aïda Ouangraoua, Marie-France Sagot, Michel Termier, Claude Thermes, Hélène Touzet

Bibliographic record

VenueAdvances in Bioinformatics · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsSimon Fraser UniversityPacific Institute for the Mathematical SciencesUniversity of British Columbia
FundersDivision of Mathematical SciencesNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueAgence Nationale de la RecherchePacific Institute for the Mathematical Sciences
KeywordsBenchmarkingComputer sciencePairwise comparisonNucleic acid secondary structureRNASoftwareData miningGenomeComputational biologyInformation retrievalTheoretical computer scienceArtificial intelligenceBiologyGeneticsGene

Abstract

fetched live from OpenAlex

The pairwise comparison of RNA secondary structures is a fundamental problem, with direct application in mining databases for annotating putative noncoding RNA candidates in newly sequenced genomes. An increasing number of software tools are available for comparing RNA secondary structures, based on different models (such as ordered trees or forests, arc annotated sequences, and multilevel trees) and computational principles (edit distance, alignment). We describe here the website BRASERO that offers tools for evaluating such software tools on real and synthetic datasets.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.006
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0050.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.012

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 head0.011
GPT teacher head0.278
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations15
Published2012
Admission routes2
Has abstractyes

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