Ka‐Chun Wong

This paper introduces scTranslator, a pre-trained large generative model that translates single-cell transcriptomes to proteomes, showing accuracy and utility in multiple downstream tasks.

scCAFE is a deep learning model that detects multi-scale chromatin architectural features in single-cell Hi-C data, outperforming existing methods and enabling cell-type annotation from 3D genome features.

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

UniPTM predicts multiple PTM sites on full-length protein sequences without using sliding windows, outperforming existing models and providing interpretable results.

This paper introduces PMDM, a dual diffusion model for generating 3D bioactive molecules and optimizing leads for specific protein targets.

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