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Record W2139830657 · doi:10.1093/nar/gkp820

DRYGIN: a database of quantitative genetic interaction networks in yeast

2009· article· en· W2139830657 on OpenAlexafffund
Judice L.Y. Koh, Huiming Ding, Michael Costanzo, Anastasia Baryshnikova, Kiana Toufighi, Gary D. Bader, Chad L. Myers, Brenda Andrews, Charles Boone

Bibliographic record

VenueNucleic Acids Research · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsUniversity of Toronto
FundersOntario GenomicsOntario Genomics InstituteGenome Canada
KeywordsBiologyComputational biologyGenomeAnnotationGene interactionOntologyGene ontologyInterface (matter)Genetic networkGenetic screenGeneInteraction networkGeneticsPhenotypeGene expression

Abstract

fetched live from OpenAlex

Genetic interactions are highly informative for deciphering the underlying functional principles that govern how genes control cell processes. Recent developments in Synthetic Genetic Array (SGA) analysis enable the mapping of quantitative genetic interactions on a genome-wide scale. To facilitate access to this resource, which will ultimately represent a complete genetic interaction network for a eukaryotic cell, we developed DRYGIN (Data Repository of Yeast Genetic Interactions)-a web database system that aims at providing a central platform for yeast genetic network analysis and visualization. In addition to providing an interface for searching the SGA genetic interactions, DRYGIN also integrates other data sources, in order to associate the genetic interactions with pathway information, protein complexes, other binary genetic and physical interactions, and Gene Ontology functional annotation. DRYGIN version 1.0 currently holds more than 5.4 million measurements of genetic interacting pairs involving approximately 4500 genes, and is available at http://drygin.ccbr.utoronto.ca.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.038
GPT teacher head0.353
Teacher spread0.314 · 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.

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

Citations95
Published2009
Admission routes2
Has abstractyes

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