MétaCan
Menu
Back to cohort
Record W2989451149 · doi:10.1371/journal.pone.0221796

Protocol development for discovery of angiogenesis inhibitors via automated methods using zebrafish

2019· article· en· W2989451149 on OpenAlexafffund
Antonio Mauro, Robin Ng, Jamie Yuanjun Li, Rui Guan, Youdong Wang, Krishna K. Singh, Xiao‐Yan Wen

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsWestern UniversityUniversity of TorontoSt. Michael's Hospital
FundersQuébec Consortium for Drug DiscoveryOntario Centres of Excellence
KeywordsZebrafishAngiogenesisDrug discoveryComputational biologyBiologyLimitingBioinformaticsCancer researchBiochemistryGeneEngineering

Abstract

fetched live from OpenAlex

Their optical clarity as larvae and embryos, small size, and high fecundity make zebrafish ideal for whole animal high throughput screening. A high-throughput drug discovery platform (HTP) has been built to perform fully automated screens of compound libraries with zebrafish embryos. A Tg(kdrl:EGFP) line, marking endothelial cell cytoplasm, was used in this work to help develop protocols and functional algorithms for the system, with the intent of screening for angiogenesis inhibitors. Indirubin 3' Monoxime (I3M), a known angiogenesis inhibitor, was used at various concentrations to validate the protocols. Consistent with previous studies, a dose dependant inhibitory effect of I3M on angiogenesis was confirmed. The methods and protocols developed here could significantly increase the throughput of drug screens, while limiting human errors. These methods are expected to facilitate the discovery of novel anti-angiogenesis compounds and can be adapted for many other applications in which samples have a good fluorescent signal.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.012

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 head0.362
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuePLoS ONESame topicZebrafish Biomedical Research ApplicationsFrench-language works237,207