Using Zebrafish to Model Autism Spectrum Disorder: A Comparison of ASD Risk Genes Between Zebrafish and Their Mammalian Counterparts
Bibliographic record
Abstract
Autism spectrum disorders (ASDs) are a highly variable and complex set of neurological disorders that alter neurodevelopment and cognitive function, which usually presents with social and learning impairments accompanied with other comorbid symptoms like hypersensitivity or hyposensitivity, or repetitive behaviors. Autism can be caused by genetic and/or environmental factors and unraveling the etiology of ASD has proven challenging, especially given that different genetic mutations can cause both similar and different phenotypes that all fall within the autism spectrum. Furthermore, the list of ASD risk genes is ever increasing making it difficult to synthesize a common theme. The use of rodent models to enhance ASD research is invaluable and is beginning to unravel the underlying molecular mechanisms of this disease. Recently, zebrafish have been recognized as a useful model of neurodevelopmental disorders with regards to genetics, pharmacology and behavior and one of the main foundations supporting autism research (SFARI) recently identified 12 ASD risk genes with validated zebrafish mutant models. Here, we describe what is known about those 12 ASD risk genes in human, mice and zebrafish to better facilitate this research. We also describe several non-genetic models including pharmacological and gnotobiotic models that are used in zebrafish to study ASD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".