Wibke Busch

MeSyTo provides an ontology-driven framework for harmonizing metadata across toxicological omics to improve reproducibility and data integration.

The paper uses Bayesian hierarchical ODE models to integrate sparse, multivariate toxicity data from zebrafish embryos, improving parameter estimation despite experimental variability and model inadequacy.

A structured zebrafish embryo toxicity database (INTOB, 638 experiments) enabled control-quality tests, dose-response modeling and phenotype-fingerprint clustering of 47 substances (133 experiments); literature data proved unsuitable for meta-analysis.

This paper introduces a framework using CGPD tetramers to group chemicals by molecular mechanisms, showing strong overlap with existing pesticide groups and identifying new compounds for risk assessment.

The paper introduces deepFPlearn, a deep learning tool that predicts chemical-effect associations at the gene/pathway level using unlabeled chemical data and achieves high prediction quality.