MétaCan
Menu
Back to cohort
Record W2121907028 · doi:10.1186/gb-2010-11-3-r30

Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action

2010· article· en· W2121907028 on OpenAlexafffund
Maureen E. Hillenmeyer, Elke Ericson, Ronald W. Davis, Corey Nislow, Daphne Koller, Guri Giaever

Bibliographic record

VenueGenome biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsCanada Research ChairsUniversity of TorontoUniversity of New Brunswick
FundersCanadian Institutes of Health ResearchNational Human Genome Research InstituteUniversity of TorontoNational Science Foundation
KeywordsFitness functionGenomeGeneComputational biologyYeastFunction (biology)BiologyDrug actionGenetic FitnessDrugGeneticsComputer scienceMachine learningPharmacology

Abstract

fetched live from OpenAlex

We systematically analyzed the relationships between gene fitness profiles (co-fitness) and drug inhibition profiles (co-inhibition) from several hundred chemogenomic screens in yeast. Co-fitness predicted gene functions distinct from those derived from other assays and identified conditionally dependent protein complexes. Co-inhibitory compounds were weakly correlated by structure and therapeutic class. We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing. Fitness data provide a novel, systems-level perspective on the cell.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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 designSimulation or modeling
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

Citations118
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueGenome biologySame topicBioinformatics and Genomic NetworksFrench-language works237,207