Ruoqiao Chen

InsilicoCell predicts cellular functional profiles and enables multi-objective virtual drug screening by integrating diverse molecular data and tasks.

This paper introduces RT-DMF, a deep learning framework to correct noise and impute missing values in pharmacogenomic datasets, improving downstream analysis.

The paper uses single-cell transcriptomics to identify S100 gene expression patterns and cell-type changes in MODS patients, showing diagnostic potential and therapeutic implications.

SPIDER predicts abundance of over 2500 surface proteins from single-cell transcriptomes using a context-agnostic zero-shot deep ensemble model, outperforming existing methods and enabling applications in cancer research.

TransCell predicts genomic and cellular responses from gene expression using transfer learning, achieving strong performance in drug sensitivity and gene effect score prediction.