Artificial Intelligence in Oncology: Program
Program
Time |
Format |
Topic |
Speakers |
|
5 Minutes |
Podium Presentation |
Introduction |
Yves Lussier, University of Utah |
|
22 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
|
|
22 Minutes |
Podium Presentation |
Middle Keynote: Unraveling Cancer Recurrence: Machine Learning for Predictive Modeling
|
Ece Uzun, Brown University |
|
22 Minutes |
Podium Presentation |
Middle Keynote: Using AI to Win the Race Against Cancer |
Peter Elkin, University at Buffalo
|
|
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 Changes in Inter-Tissue Transcriptome Coordination During Aging
|
Hongfang Liu, UTHealth Alper Uzun, Brown University
Davide Chicco, Universit` a di Milano-Bicocca
Judith Somekh, University of Haifa |
|
22 Minutes |
Podium Talk |
Closing Keynote: Enabling AI in Medicine requires both EHRs and Humans-in-the-loop |
James L. Chen, MD, Ohio State University
|