About me

Dr. Danh-Tai Hoang is a Research Scientist II at Cedars-Sinai Medical Center.

His research focuses on developing novel AI frameworks for personalized cancer medicine. In particular, he led the development of: (i) DeepPT, a deep learning model that predicts RNA-seq gene expression from pathology slides (Hoang et al., Nature Cancer 2024); and (ii) DEPLOY, a deep learning model that predicts DNA methylation from pathology slides (Hoang et al., Nature Medicine 2024). More recently, he developed Path2Omics, an advanced framework building on DeepPT and DEPLOY. This study showed that the gene expression inferred by Path2Omics closely matches measured expression for predicting patient survival and treatment response (Hoang et al., Cancer Research 2025). Path2Omics has since been incorporated into subsequent studies for predicting response to neoadjuvant therapies (BRIDGE; Cantore et al, Annals of Oncology, 2026) and immunotherapies (TIME_ACT; Mukherjee, et al., BioRxiv 2025).

Looking ahead, his work aims to develop models capable of predicting proteomics/transcriptomics at the single-cell/spot level from pathology slides. These efforts are directed toward identifying spatially resolved biomarkers for improving predictions of patient survival and treatment response.

Prior to joining Cedars-Sinai, he served as a Staff Scientist at the Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH, USA), and a Research Fellow (Academic Staff Level B) at the Biological Data Science Institute (BDSI), Australian National University (ANU). He received a PhD degree in Theoretical Physics from CY Cergy Paris University (France).

*** Research Focus **

• Digital Pathology
• Multimodal Deep Learning
• Cancer Immunology and Immunotherapy
• Personalized Cancer Medicine
• Spatial Omics