High-Throughput Behavioral Screens: the First Step towards Finding Genes Involved in Vertebrate Brain Function Using Zebrafish
Bibliographic record
Abstract
The zebrafish has been in the forefront of developmental biology for three decades and has become a favorite of geneticists. Due to the accumulated genetic knowledge and tools developed for the zebrafish it is gaining popularity in other disciplines, including neuroscience. The zebrafish offers a compromise between system complexity (it is a vertebrate similar in many ways to our own species) and practical simplicity (it is small, easy to keep, and prolific). Such features make zebrafish an excellent choice for high throughput mutation and drug screening. For the identification of mutation or drug induced alteration of brain function arguably the best methods are behavioral test paradigms. This review does not present experimental examples for the identification of particular genes or drugs. Instead it describes how behavioral screening methods may enable one to find functional alterations in the vertebrate brain. Furthermore, the review is not comprehensive. The behavioral test examples presented are biased according to the personal interests of the author. They will cover research areas including learning and memory, fear and anxiety, and social behavior. Nevertheless, the general principles will apply to other functional domains and should represent a snapshot of the rapidly evolving behavioral screening field with zebrafish.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".