Aaron M. Scott

Inferring the composition of the blood plasma proteome by a human proteome distribution atlas

The paper develops a proteomics strategy to infer the origin of proteins in human plasma using a proteome atlas and RNA/protein atlases, demonstrating its application in disease cohorts.

Community-based peptidomic clustering reveals pathogen-specific proteolytic signatures in wounds

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

Explainable machine learning for the identification of proteome states via the data processing kitchen sink

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

Interpreting biologically informed neural networks for enhanced biomarker discovery and pathway analysis

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

Generalized peakgroup scoring boosts identification rates and accuracy in mass spectrometry based discovery proteomics

This paper introduces GPS, a machine learning-based method for improving peptide and protein identification accuracy and rates in DIA proteomics by using an external dataset, and demonstrates its effectiveness in identifying biomarkers for acute kidney injury.