The following represents disclosure information provided by authors of this abstract. The program committee has reviewed all presenting author disclosure reports, identified potential conflicts of interest, and implemented strategies to manage those areas of conflict, where appropriate. All relationships are considered compensated. Relationships are self-held unless otherwise noted. I = Immediate Family Member, Inst = My Institution
 
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Predicting chemotherapy benefit in premenopausal women with intermediate genomic scores using deep learning.
 
Arkadi Piven
No Relationships to Disclose
 
Yoav Binenbaum
Research Funding - Boston Childrens Hospital TRP Pilot; NIH T32
Patents, Royalties, Other Intellectual Property - EP3222121B1
 
Dvir Aran
Stock and Other Ownership Interests - Link Cell Therapies
Consulting or Advisory Role - Carelon Digital Platforms; Link Cell Therapies
 
Joseph Sparano
Consulting or Advisory Role - AstraZeneca; Genentech/Roche; General Dynamics Information Technology (GDIT); Lyell Immunopharma; Novartis; NPJ Breast Cancer; Pfizer; UpToDate
Research Funding - Olema Pharmaceuticals (Inst)
 
Ron Kimmel
Research Funding - ISF – Israel Precision Medicine Partnership 2021-2025; Israel Cancer Research Fund 2024-2027; Technion t:start 2025 2026; Vatat – Ministry of Innovation Science and Technology 2025-2027; Zimin Institute 2025-2026
Patents, Royalties, Other Intellectual Property - CLASSIFICATION OF CANCER FOR TREATMENT AND/OR MANAGEMENT BASED ON MACHINE LEARNING MODELS
 
Gil Shamai
Patents, Royalties, Other Intellectual Property - CLASSIFICATION OF CANCER FOR TREATMENT AND/OR MANAGEMENT BASED ON MACHINE LEARNING MODELS