AI x Variant Interpretation

Join us for insightful discussions on the thrilling intersection of AI and genomic variant interpretation! This event features technical talks addressing engineering challenges and innovative approaches that push the boundaries of our understanding in genomics.

🎤 Key Presentations Include:

• Ruchir Rastogi (Postdoctoral Scholar @ Kundaje Lab, Stanford)

  • Topic: Using sequence-to-function models to predict molecular traits.
    Explore how models like Enformer and AlphaGenome are designed to predict molecular traits but face challenges in accuracy when explaining differences in gene expression driven by personal mutations.

• Shiron Drusinsky (Bioinformatics PhD Candidate, Pollard Lab @ UCSF/Gladstone)

  • Topic: Challenges in deep learning prediction of gene expression from genetic variants.
    Delve into the intricacies of why current models struggle to predict expression differences and discover potential pathways for improvement.

• Sayan Ghosal (Senior Research Scientist, AI/ML @ Chan Zuckerberg Initiative)

  • Topic: VariantFormer: Integrating DNA sequences with genetic variations.
    Unveil a hierarchical transformer model that promises accurate predictions for personalized gene expression across genetic variations and regulatory landscapes.

• Esther Robb (CS PhD Candidate, Montgomery Lab @ Stanford)

  • Topic: Mapping gene-by-exercise effects using multi-omics.
    Learn how exercise influences genetic responses and how genetic traits play a role in exercise outcomes using groundbreaking multi-omics data.

🗓️ Event Agenda:

• 6:00 PM - 6:30 PM: Networking with refreshments.
• 6:30 PM - 8:00 PM: Expert talks followed by Q&A.
• 8:00 PM - 9:00 PM: Time to socialize and discuss further!

🔗 Secure your spot: Register Here!

Don't miss out on this innovative gathering of minds at the forefront of AI and genomics!