Qingpeng Zhang

This study analyzes longitudinal electronic health records to identify comorbidity patterns influenced by COVID-19 and their biological mechanisms through protein-protein interaction networks.

BioXNet predicts anti-cancer drug responses by integrating drug targets and genomic profiles, showing better performance than existing models and revealing differences in response mechanisms between cell lines and humans.

The paper introduces ERTransformer, a large language model that generates T-cell receptors with desired epitope-binding properties, showing high functional similarity to natural TCRs.

This paper develops tensor factorization-based models to predict individual chronic disease risk using EHR data, achieving 60% recall in top 10 predictions.