Darien Yeung

The paper introduces MoSTERT and MoSTERT-2S, transformer-based models for zero-shot retention time prediction of peptides with unseen post-translational modifications, achieving state-of-the-art performance on an external dataset.

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.