Genetics and Machine Learning in Epilepsy: Discovery and Applications
Clinical Neurology, Artificial Intelligence, Bio-Informatics and workshop
Sunday
About
This workshop connects genomic discovery with prediction of epilepsy outcomes. It examines genome-based multi-omics in multigenerational cohorts, multimodal deep learning that combines clinical and genomic information to predict antiseizure medication response, and polygenic risk scores integrated with machine learning to estimate disease severity and seizure control. The presentations link these complementary approaches to the problem of clinical heterogeneity and assess their practical role in more individualized treatment.
Sunday 6 December, 14:00–15:30 Mountain Standard Time (America/Denver). Part of the 2026 American Epilepsy Society Annual Meeting. In-person meeting registration and a free myAES account are required. Standard full-meeting rates are USD 800 for member scientists and USD 1,075 for non-member scientists; student rates are USD 250 and USD 360, respectively. Other professional and one-day categories are available. Seating priority goes to registered attendees.
Guest speakers
- Yi Li, Stanford University
- Chris McGraw
- Daniel Lowenstein, University of California San Francisco
- Patrick Kwan, Monash Institute of Medical Engineering, Monash University
- Takafumi Kubota, Tohoku University Graduate School of Medicine
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