Jörg Hackermüller A

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

The paper develops a framework for multi-omics WGCNA and differential network analysis to study thyroid toxicity, finding extensive molecular disruption and partial recovery.

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.

This study benchmarks scRNA-seq imputation tools for network inference, finding they perform well on low/moderate sparsity but fail at high sparsity.

multiGSEA combines GSEA-based pathway enrichment across multiple omics layers using 8 pathway databases and supports 11 organisms.

The paper introduces uap, a workflow management system for reproducible and robust HTS data analysis, addressing reproducibility challenges in complex bioinformatic pipelines.