Caren C. Helbing

The paper introduces triAMPh, a framework for predicting pathogen-specific antimicrobial peptide activity using biological large language models, showing it matches or outperforms existing methods.

This study evaluates three AI-identified antimicrobial peptides for efficacy and safety against avian pathogenic Escherichia coli in poultry, showing improved survival and immune modulation without adverse effects.

The paper identifies novel antimicrobial peptides from the UniProtKB/Swiss-Prot database using AMPlify, prioritizing 40 for chicken applications and testing 38 for antimicrobial activity.

This study measures thyroid hormone levels and gene expression in Rana catesbeiana during metamorphosis and identifies metabolite bioindicators of developmental changes.

AMPlify is a deep learning model that predicts antimicrobial peptides from bullfrog genomes, identifying novel peptides effective against WHO priority pathogens including drug-resistant bacteria.

The paper reports the first draft genome of the North American bullfrog, providing genomic resources for amphibian research and supporting taxonomic classification.