Advances in Zebrafish High Content and High Throughput Technologies
Bibliographic record
Abstract
The zebrafish has emerged as an excellent transitional screening model system between cell-based assays, which are rapid and inexpensive but have limited physiological relevance, and higher vertebrate models, which have better physiological relevance, but are more time-consuming and expensive to deploy. As vertebrates, zebrafish maintain significant evolutionary proximity to humans and have been validated as robust models for drug research, studies of mechanism and behavioral genetics. Unlike higher vertebrate models, zebrafish are well-suited to high-throughput applications owing to their high fecundity, rapid extrauterine development and transparency during organogenesis enabling in vivo labeling and imaging. Recent advances have been made in automating high content and high-throughput zebrafish screens, with the goal of developing fully automated drug screening platforms. The application and continued development of these technologies holds potential clinical significance in drug discovery and elucidating disease mechanisms. Keywords: Chemical biology, drug development, high content, high throughput screen, pharmacogenomics, system automation, zebrafish, fully automated drug screening, Gene knockdown, Danio rerio
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".