Kristin Schirmer

The paper profiles PAC2 zebrafish cell line across growth phases using proteomics and transcriptomics to assess its utility for toxicological studies.

This paper identifies criteria for reliable comparison between predictive ecotoxicology studies and highlights the lack of compliance with these criteria in existing research.

This study evaluates the sensitivity of daphnids compared to fish for neurotoxic chemicals, finding daphnids often more sensitive and suggesting their use as an alternative to fish toxicity tests.

The paper examines interspecific differences in biotransformation capacity among fish species in polluted waters and links these differences to biodiversity impacts.

This study trains and evaluates machine learning models to predict fish acute mortality from chemical properties, finding that tree-based models perform best but struggle with individual chemical predictions.

The paper introduces ADORE, a benchmark dataset for machine learning in ecotoxicology, to facilitate model comparison and challenge researchers to improve predictive performance.