Paulina Szymczak

OmegAMP generates antimicrobial peptides with controlled physicochemical properties and safety profiles, showing activity against drug-resistant bacteria in vitro and in vivo.

BATTLE-AMP benchmarks antimicrobial peptide predictors using MIC data, showing that models trained on MIC data outperform binary classifiers and highlighting limitations in distinguishing active from inactive peptides.

This study identifies distinct pneumonia endotypes based on pathogen-specific host responses in the lung, linking them to clinical outcomes and suggesting personalized treatment strategies.

The paper integrates kidney and urine proteomics with peptidomics and machine learning to identify encrypted antimicrobial peptides as functional effectors in human pyelonephritis.

PepGlider enables precise control over peptide properties through a continuous regularization framework, allowing for de novo generation and targeted optimization of peptides with desired characteristics.

Celloscope deconvolves cell types in spatial transcriptomics data using marker genes, outperforming existing methods and accurately identifying brain structures and immune infiltrates.

This paper introduces HydrAMP, a deep generative model for creating potent antimicrobial peptides, which outperforms existing methods and validates nine new peptides in lab tests.