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Record W2139866374 · doi:10.1186/gm549

A genome scale overexpression screen to reveal drug activity in human cells

2014· article· en· W2139866374 on OpenAlex
Anthony Arnoldo, Saranya Kittanakom, Lawrence E. Heisler, Anthony B. Mak, Andrey I. Shukalyuk, Dax Torti, Jason Moffat, Guri Giaever, Corey Nislow

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGenome Medicine · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Toronto
FundersNational Human Genome Research Institute
KeywordsHuman genomeComputational biologyHuman geneticsORFSDrugDrug discoveryDrug actionDrug resistanceGeneGenomeBiologyTranscription factorGeneticsBioinformaticsPharmacology

Abstract

fetched live from OpenAlex

Target identification is a critical step in the lengthy and expensive process of drug development. Here, we describe a genome-wide screening platform that uses systematic overexpression of pooled human ORFs to understand drug mode-of-action and resistance mechanisms. We first calibrated our screen with the well-characterized drug methotrexate. We then identified new genes involved in the bioactivity of diverse drugs including antineoplastic agents and biologically active molecules. Finally, we focused on the transcription factor RHOXF2 whose overexpression conferred resistance to DNA damaging agents. This approach represents an orthogonal method for functional screening and, to our knowledge, has never been reported before.

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.

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)0.000
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 score0.186
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.277
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