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Record W4200320070 · doi:10.1093/femsyr/foab060

A data library of <i>Candida albicans</i> functional genomic screens

2021· article· en· W4200320070 on OpenAlexafffund
Nicholas C Gervais, Viola Halder, Rebecca S. Shapiro

Bibliographic record

VenueFEMS Yeast Research · 2021
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research AssociationCanadian Geriatrics Society
KeywordsBiologyCandida albicansComputational biologyMicrobiology

Abstract

fetched live from OpenAlex

Functional genomic screening of genetic mutant libraries enables the characterization of gene function in diverse organisms. For the fungal pathogen Candida albicans, several genetic mutant libraries have been generated and screened for diverse phenotypes, including tolerance to environmental stressors and antifungal drugs, and pathogenic traits such as cellular morphogenesis, biofilm formation and host-pathogen interactions. Here, we compile and organize C. albicans functional genomic screening data from ∼400 screens, to generate a data library of genetic mutant strains analyzed under diverse conditions. For quantitative screening data, we normalized these results to enable quantitative and comparative analysis of different genes across different phenotypes. Together, this provides a unique C. albicans genetic database, summarizing abundant phenotypic data from functional genomic screens in this critical fungal pathogen.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.154
GPT teacher head0.376
Teacher spread0.222 · 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 designObservational
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

Citations12
Published2021
Admission routes2
Has abstractyes

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