AI x Variant Interpretation

Technical talks on engineering challenges and interesting problems at the intersection of AI and genomic variant interpretation. Talks cover sequence-to-function models for predicting molecular traits, personalized gene expression from individual genomes, deep learning for variant effect prediction, and benchmarks + evaluations for frontier models in genomics. We'll hear from the following: Ruchir Rastogi — Postdoctoral Scholar @ Kundaje Lab, Stanford Using sequence-to-function models to predict personal molecular traits Sequence-to-function models like Enformer and AlphaGenome predict molecular readouts like transcription factor binding, chromatin accessibility, gene expression directly from DNA sequence, making them promising tools for predicting how mutations affect those traits. But work from our group and others shows they aren't yet accurate enough to explain expression differences between individuals from personal mutations: current models underperform simple linear baselines and are sometimes strongly negatively correlated with measured data. We give an overview of potential…