This research investigates macrophages, immune cells that regulate infection, tissue repair, and cancer responses. Through laboratory experiments and machine-learning models, it aims to predict macrophage function across different diseases and patients. The work could improve prognosis, guide treatments, evaluate drug safety, and forecast recovery following major illnesses and injuries.

This research applies large language models to decode and design proteins by treating amino acid sequences as biological languages. By identifying hidden structural and functional patterns across massive protein datasets, the work enables creation of novel proteins for medicine, cancer therapy, carbon capture, and environmental remediation beyond naturally evolved biological systems.

This research develops synthetic genetic circuits that automatically alternate CAR T-cell activity between active cancer killing and recovery states. By preventing immune-cell exhaustion, these circuits could improve cancer immunotherapy effectiveness. The work also suggests broader biomedical applications where controlled cycling of gene activity may enhance treatment safety, longevity, and therapeutic performance.

This research uses natural language processing techniques to uncover evolutionary relationships between ancient proteins. By analyzing contextual patterns among amino acids, the new computational tool can identify connections between proteins that diverged billions of years ago, helping scientists reconstruct the history of early microbial life and Earth’s biological evolution.

This research investigates the protein SLX4, a key coordinator of DNA repair. Using complementary techniques, it identifies 221 interacting proteins, most previously unknown. Findings reveal a complex network involved in genome maintenance, offering new insights into cellular repair mechanisms and improving understanding of diseases such as cancer.

This research develops a computational method for detecting hidden RNA viruses within existing RNA sequencing datasets. By identifying conserved viral protein signatures, the approach enables large-scale discovery of previously unknown viruses, improving understanding of viral diversity, disease mechanisms, and future opportunities for diagnostics, surveillance, and antiviral treatment development.