Integrating network analysis and machine learning to elucidate chemical-induced pancreatic toxicity in zebrafish embryos
Bibliographic record
Abstract
Zebrafish (Danio rerio) are a popular vertebrate model for high-throughput toxicity testing, serving as a model for embryonic development and disease etiology. However, standardized protocols using zebrafish tend to explore pathologies and behaviors at the organism level rather than at the organ-specific level. This study investigates the effects of chemical exposures on pancreatic function in whole-embryo zebrafish by integrating network analysis and machine learning, leveraging widely available datasets to probe an organ-specific effect. We compiled transcriptomics data for zebrafish exposed to 53 exposures from 25 unique chemicals, including halogenated organic compounds, pesticides/herbicides, endocrine-disrupting chemicals, pharmaceuticals, parabens, and solvents. All raw sequencing data were processed through a uniform bioinformatics pipeline for re-analysis and quality control, identifying differentially expressed genes and altered pathways related to pancreatic function and development. Clustering analysis revealed 5 distinct clusters of chemical exposures with similar impacts on pancreatic pathways, with gene co-expression network analysis identifying key driver genes within these clusters, providing insights into potential biomarkers of chemical-induced pancreatic toxicity. Machine learning was utilized to identify chemical properties that influence pancreatic pathway response, including average mass and biodegradation half-life. The random forest model achieved robust performance (4-fold cross-validation accuracy: 74%) over eXtreme Gradient Boosting, support vector machine, and multiclass logistic regression. This integrative approach enhances our understanding of the relationships between chemical properties and biological responses in a target organ, supporting the use of zebrafish whole embryos as a high-throughput vertebrate model. This computational workflow can be leveraged to investigate the complex effects of other exposures on organ-specific development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".