Dynamic Digital Tumors for Precision Oncology

We are developing integrated digital tumor models and advanced AI technologies to revolutionize personalized cancer treatment

Dynamic Digital Tumors for Precision Oncology is a collaborative project focused on transforming cancer care through next-generation AI technologies. This initiative aims to develop advanced Drug Recommender Engines (DREs) that match patients with breast, lung, or colon cancer to the most effective therapies. One key innovation is the Genotype-to-Phenotype Transformer (G2PT), which models complex relationships between genetic variants and disease traits to enable fine-grained prediction and interpretation of drug response. By integrating data from genomics, health records, and medical imaging, these models will predict therapy outcomes, explain drug resistance, and adapt dynamically as new information becomes available. 

Meet the Team

Get to know the brilliant minds behind Dynamic Digital Tumors for Precision Oncology.

Contact Us
hello@digitaltumors.org

Trey Ideker
Principal Investigator
Professor of Medicine
UC San Diego

Jillian Parker
UC San Diego
jillianparker@ucsd.edu

Aritro Nath
City of Hope
anath@coh.org

Su-In Lee

Vineet Bafna
UCSD
vbafna@ucsd.edu

Benjamin Haibe-Kains 

Jason Griffiths

Nevan Krogan
UC San Francisco 

Stephanie Huang

Max Sherman
Serinus Biosciences, Inc