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Record W2149849221 · doi:10.1093/toxsci/kfl131

Chemical Genomic Profiling for Identifying Intracellular Targets of Toxicants Producing Parkinson's Disease

2006· article· en· W2149849221 on OpenAlexaff
Julie Doostzadeh, Ronald W. Davis, Guri Giaever, Corey Nislow, J. William Langston

Bibliographic record

VenueToxicological Sciences · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsUniversity of Toronto
FundersNational Institutes of Health
KeywordsParaquatToxicogenomicsGeneYeastBiologySaccharomyces cerevisiaeMutantGeneticsToxicityComputational biologyBiochemistryChemistryGene expression

Abstract

fetched live from OpenAlex

The yeast deletion collection includes approximately 4700 strains deleted for both copies of every nonessential gene. This collection is a powerful resource for identifying the cellular pathways that functionally interact with drugs. In the present study, the complete pool of approximately 4700 barcoded homozygous deletion strains of Saccharomyces cerevisiae were surveyed to identify genes/pathways interacting with 1-methyl-4-phenylpyridinium (MPP(+)) and N,N-dimethyl-4-4-bipiridinium (paraquat), neurotoxicants that can produce Parkinson's disease. Each yeast mutant is molecularly "barcoded" the collections can be grown competitively and ranked for sensitivity by microarray hybridization. Analysis data from these screens allowed us to determine that the multivesicular body pathway is an important element of toxicity induced by both MPP(+) and paraquat. When yeast genes that when deleted showed sensitivity to MPP(+) and paraquat toxicity were analyzed for their homology to human genes, 80% were found to have highly conserved human homologs (with e < 10(-8)). Future work will address if these human genes may also functionally interact with MPP(+) and paraquat toxicity.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.342

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 head0.041
GPT teacher head0.313
Teacher spread0.271 · 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

Citations16
Published2006
Admission routes1
Has abstractyes

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