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Record W2394996744 · doi:10.1007/978-1-61779-173-4_15

The Automated Cell: Compound and Environment Screening System (ACCESS) for Chemogenomic Screening

2011· article· en· W2394996744 on OpenAlexafffund
Michael Proctor, Malene L. Urbanus, Eula Fung, Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow, Guri Giaever

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAutomationComputer scienceDrug discoverySet (abstract data type)Computational biologyBioinformaticsBiologyEngineering

Abstract

fetched live from OpenAlex

The automated cell, compound and environment screening system (ACCESS) was developed as an automated platform for chemogenomic research. In the yeast Saccharomyces cerevisiae, a number of genomic screens rely on the modulation of gene dose to determine the mode of action of bioactive compounds or the effects of environmental/compound perturbations. These and other phenotypic experiments have been shown to benefit from high-resolution growth curves and a highly automated controlled environment system that enables a wide range of multi-well assays that can be run over many days without any manual intervention. Furthermore, precise control of drug dosing, timing of drug exposure, and precise timing of cell harvesting at specific generation times are important for optimal results. Some of these benefits include the ability to derive fine distinctions between growth rates of mutant strains (1) and the discovery of novel compounds and drug targets (2). The automation has also enabled large-scale screening projects with over 100,000 unique compounds screened to date including a thousand genome-wide screens (3). The ACCESS system also has a diverse set of software tools to enable users to set up, run, annotate, and evaluate complex screens with minimal training.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.163
Threshold uncertainty score0.622

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 science0.0000.001
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 head0.052
GPT teacher head0.375
Teacher spread0.323 · 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
GenreMethods

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

Citations27
Published2011
Admission routes2
Has abstractyes

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