Jing Li

This paper introduces TMCA, a multimodal framework that integrates SMILES, molecular graphs, and 3D conformations for molecular property prediction, achieving the best performance on benchmark datasets.

This paper proposes a joint learning framework for predicting drug combinations and drug-drug interactions using coupled tensor-tensor factorization with side information, achieving strong performance on new-drug prediction.

DrugLM enhances drug-target interaction predictions by integrating textual embeddings from large language models into DTI models, showing strong performance without fine-tuning.

The paper identifies novel structural variants in Han Chinese genomes using long-read sequencing and links them to phenotypic diversity and disease susceptibility.

The paper compares twelve methods for inferring gene co-expression networks and identifying functional communities in yeast, finding that CLR followed by Generalized Modularity Density clustering performs best.