Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …

Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big
concern during drug development in the pharmaceutical industry. Failure or inhibition of …

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …

Adoptive cell therapy (ACT) with tumor-specific T cells has been shown to mediate durable
cancer regression. Tumor-specific T cells are also the basis of other therapies, notably …

Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to
developing personalized cancer vaccines. Experimental validation of candidate …

Motivation Single-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to
infer cell-specific gene regulatory networks (GRNs), which is an important challenge in …

The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and
result in adverse consequence to the patients. Accurate identification of DDI types can not …

The combination of library-based screening and artificial intelligence (AI) has been
accelerating the discovery and optimization of hit ligands. However, the potential of AI to …

Self-supervised representation learning (SSL) on biomedical networks provides new
opportunities for drug discovery; however, effectively combining multiple SSL models is still …

Autophagy is a cellular process in which proteins and organelles are engulfed in
autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein …