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Record W2588206635 · doi:10.5114/bta.2016.62927

Cell-based assays in high-throughput mode (HTS)

2016· article· en· W2588206635 on OpenAlex

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

VenueBioTechnologia · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsThroughputComputer scienceMode (computer interface)Computational biologyBiologyOperating system

Abstract

fetched live from OpenAlex

Typically, novel compounds are screened by the millions, a process known as high-throughput screening (HTS). HTS allows for the screening of millions of potential drugs in a relatively short period of time. All compounds are initially subjected to various tests to determine safety and efficacy. At the molecular level, typically two types of tests are available: in vitro and cell-based assays (i.e., in vivo ). The distinction between a cell-based assay and an in vitro screening is that the cell-based assay utilizes live cells – approximately 50 000 cells are seeded onto the floor of the well. Cell-based assays are used to measure proliferation, toxicity, marker production, motility, activation of signaling pathways, and changes in morphology. In such cases, other factors such as 2D versus 3D culture or static versus profusion cultures might also contribute to the results obtained. This study emphasizes the positive aspects of using cell-based assays in high-throughput mode.

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 score0.084
Threshold uncertainty score0.555

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 integrity0.0010.000
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 head0.005
GPT teacher head0.238
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