Artificial Intelligence in Oncology: Program
Program
Time |
Format |
Topic |
Speakers |
|
5 Minutes |
Podium Presentation |
Introduction |
Yves Lussier, University of Utah |
|
30 Minutes |
Podium Presentation |
Opening Keynote: Single-cell unified polarization assessment of immune cells using single cell foundation model |
Zhongming Zhao, UTHealth |
|
40 Minutes |
Research presentations |
MLPA: A Multi-scale Digital Twin Framework for Personalized Cancer Simulation and Treatment Optimization Paired-sample and Pathway-Anchored MLOps for Robust Transcriptomic Machine Learning in Small Cohort. Case report in classifying TP53 vs PIK3CA-driven breast cancers AI and large language models to advance oncology research |
Jake Chen, University of Alabama
Yves Lussier, University of Utah
Rui Zhang, University of Minnesota |
|
15-20 Minutes |
Break |
Connect and collaborate |
N/A
|
|
30 Minutes |
Podium Presentation |
Middle Keynote: Unraveling Cancer Recurrence: Machine Learning for Predictive Modeling |
Ece Uzun, Brown University |
|
40 Minutes |
Research presentations |
TBD ASCEND: An AI-powered Framework for Integrating Methylation and Transcriptomics in Oncology DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone
|
Hongfang Liu, UTHealth Alper Uzun, Brown University
Davide Chicco, Universit` a di Milano-Bicocca
|
|
30 Minutes |
Podium Talk |
Closing Keynote: Enabling AI in Medicine Requires both EHRs and Humans-in-the-loop |
James L. Chen, MD, Ohio State University
|