Charlotte Adams

TreeMS2 constructs molecular phylogenies from raw MS/MS data, revealing relationships based on molecular phenotype and identifying deviations from genetic-based trees.

This paper discusses machine learning strategies to overcome data challenges in mass spectrometry-based proteomics, emphasizing the need for standardized datasets and larger data sizes to improve model performance.

The paper trains a deep learning model to predict fragment ion intensities, improving immunopeptide identification rates using timsTOF instruments.

The paper reanalyzes public mass spectrometry data to identify post-translational modifications in SARS-CoV-2-host protein interactions, revealing novel modification sites and their potential functional roles.