Victor Spicer

SysQuan uses SILAC mouse tissues as internal standards to enable affordable absolute quantitation of the human proteome, improving comparability across studies and laboratories.

The paper introduces Chronologer, a deep learning tool for accurate peptide retention time prediction, outperforming existing methods by leveraging harmonized datasets of post-translational modifications.

The paper develops a sequence-specific model to predict peptide collision cross-section values using amino-acid sequence and charge state information from proteomic data.

The paper integrates proteomes from lung tissue and BALF in allergen-challenged mice to identify compartment-specific and shared biological pathways involved in allergic asthma responses.