Mahsa Ghanbari

Uncertainty-Aware Deep Learning for Genomics Applications: Insights from an Empirical Study

This work presents an empirical analysis of UQ in deep learning models, focusing on genomics applications, and shows that Bayesian Neural Networks are better at capturing uncertainty caused by strong class imbalance and out-of-distribution data in genomics, despite their computational disadvantages.