Tianlong Chen

CysLENS identifies cysteine ligandability by integrating chemoproteomic data with protein language models to prioritize stereoselective interactions.

The paper identifies circadian misalignment between cancer and immune cells in breast tumors as a mechanism of immune evasion and therapeutic resistance.

GatorSC improves single-cell transcriptomic analysis by integrating multi-scale cell and gene graphs with a Mixture-of-Experts fusion framework, outperforming existing methods in clustering, imputation, and annotation.

CryoNeRF improves cryo-EM structure reconstruction by handling both homogeneous and heterogeneous data, achieving better performance than existing methods.

This paper introduces a multimodal foundation model for predicting protein phenotypes using sequence, structure, and natural language, showing competitive performance and novel insights into disease-related proteins.

scMoE introduces a framework for multi-modal, multi-task single-cell learning that addresses optimization conflicts and interpretability challenges through Sparse Mixture-of-Experts and activation vectors.