Zixuan Xiao

My research addresses this challenge by rethinking how we use precursor-level (MS1) information, which is abundant but often underexploited. In this pursuit, I developed SWAPS, a framework that deconvolutes overlapping MS1 features across m/z, retention time, and ion mobility dimensions, integrates peptide property predictions, and enables peptide identity propagation (PIP) across diverse experimental conditions. 

Additionally, I work on improving protein identification and quantification in the MS1 space by reducing stochasticity in peptide-to-protein aggregation. Specifically, I leverage deep learning to generate protein fingerprints in analogy with peptide fragment intensities. 

In collaboration with a mass spectrometry vendor, I am also developing novel MS1-centric data acquisition strategies. These include precursor sampling algorithms with optimized duty cycles to enhance acquisition efficiency and maximize data quality.​ 

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