Nicholas Shulman

The paper presents two large-scale proteomics datasets from mice exposed to varying X-ray doses and time points, enabling biomarker discovery and machine learning applications for radiation exposure assessment.

Carafe2 generates high-quality in silico spectral libraries for timsTOF DIA proteomics by fine-tuning deep learning models on DIA data, outperforming DDA-trained models in multiple applications.

This paper evaluates a prototype Orbitrap Astral Zoom mass spectrometer for quantitative proteomics, showing improved performance in ion detection and quantitative precision compared to the standard Orbitrap Astral MS.

This paper introduces a method to improve quantitation in DIA proteomics by imputing peptide retention time boundaries, leading to more accurate quantifications and better estimation of radiation exposure.

The paper introduces a new MS platform with enhanced acquisition rates and real-time chromatogram alignment, enabling high multiplex targeted proteomics with improved sensitivity and throughput.

This paper introduces a workflow for improved XL-MS data analysis using PASEF, integrating MeroX and Skyline for better identification of cross-linked products.

The paper develops a multidimensional lipid library using Skyline to annotate lipidomic data and applies it to identify lipid markers for smoke inhalation injury severity.

Avant-garde improves DIA data analysis by refining signals, reducing interference, and controlling false discovery rates through a data-driven approach.