Xingjian Chen

VIP-OT traces drug response heterogeneity in single cells using vibrational imaging and optimal transport to predict post-treatment metabolic states from pre-treatment spectra.

RamanOmics integrates snRNA-seq, spatial transcriptomics, and Raman imaging to map spatial biochemical and molecular changes in aging and repair, identifying senescent cell signatures and their functional roles.

This study compares proteomics and transcriptomics for drug response prediction in cancer cell lines, finding proteomics provides better performance.

SCHAF generates spatially resolved single-cell transcriptomes from H&E histology images using vision transformers and adversarial learning, showing good agreement with ground-truth data.

DeepMotifSyn synthesizes heterodimeric DNA motifs from monomeric pairs using deep learning, outperforming existing methods on CAP-SELEX data.