Jiancong Xie

This paper introduces EvoSyn, an evolutionary multi-agent framework that combines LLM reasoning with domain validation to generate chemically feasible molecules.

CellFM is a large-scale foundation model pre-trained on 100 million human cells, outperforming existing models in cell annotation, perturbation prediction, and gene function prediction.

DrugVNN predicts drug responses with high accuracy and interpretability by integrating gene features and drug representations using a novel attention mechanism.

The paper proposes a cross-modal graph contrastive learning method that uses cellular images to improve molecular representation learning for downstream tasks.

This paper introduces a graph convolutional network to predict disease-gene associations by leveraging external collaborative graphs and reducing noise through mutual information maximization.

SPROF-GO predicts protein functions from sequences using a pretrained language model and label diffusion, outperforming existing methods by over 10% in AUPR.

The paper develops MolRep, a library unifying 16 molecular representation models, and evaluates them on 12 benchmarks to identify top-performing models for property prediction.