Yucheng Guo

DeepD is a genome foundation model that enables accurate sequence-to-function prediction and controllable genome-scale sequence generation across domains of life.

BioLab uses a multi-agent system with foundation models to autonomously design and validate antibodies, achieving superior performance compared to existing methods.

This paper introduces MorphDiff, a transcriptome-guided diffusion model that predicts cell morphology changes under perturbations and improves MOA identification.

This paper introduces modality-chain reasoning for multimodal protein modeling and design, showing improved performance in structure prediction, inverse folding, and fitness prediction compared to existing models.

This paper introduces scFoundation, a large-scale pretrained model for single-cell transcriptomics, achieving state-of-the-art results in multiple downstream tasks.