Erik Hartman

This paper introduces proteome velocity, a framework using flow matching to infer continuous proteome trajectories from proteomics data, showing its application in mouse sepsis and clinical COVID-19.

BoPep identifies peptide binders to specific proteins by efficiently navigating sequence space, reducing docking evaluations and discovering novel therapeutic peptides.

The paper develops a computational workflow to cluster peptides in peptidomic data, revealing pathogen-specific proteolytic signatures in wound fluids.

The paper introduces DPKS, an explainable machine learning framework for identifying proteome states by calculating protein importance in predictive models.

This paper introduces BINNs, a deep learning method that integrates protein abundance and pathways to improve biomarker discovery and subphenotype classification in proteomics data.

Peptimetric is a web tool for analyzing peptidomic data, enabling dynamic comparison of peptides across samples to identify differences.