Laura J. Dunphy

The paper proposes a framework to improve toxicity prediction for small molecular classes by fine-tuning a general model on class-specific data, outperforming classical methods and the EPA TEST tool.

The paper presents a generative AI pipeline for discovering antifungal peptides, identifying five with activity against fungal pathogens and low cytotoxicity.

This study uses image-based morphological profiling to detect radiation exposure signatures in fibroblasts, showing morphological changes as early as 4 hours post-exposure.

DeepPaint is a deep learning package that classifies Cell Painting images without requiring cell segmentation, achieving high accuracy in binary and multiclass classification of biological perturbations.

The paper updates a genome-scale metabolic network for Pseudomonas aeruginosa PA14 and integrates it with transcriptomic data to reveal metabolic shifts in response to mucin exposure.

This study identifies metabolic changes in antibiotic-resistant Pseudomonas aeruginosa using growth profiling and network analysis, revealing both reduced and enhanced growth on specific carbon sources.