An Empirical Analysis of Uncertainty Estimation in Genomics Applications

The usability of machine learning solutions in critical real-world applications relies on the availability of an uncertainty measure that reflects the confidence in the model predictions. In this work, we present an empirical analysis of uncertainty estimation approaches in Deep Learning models. We contrast Bayesian Neural Networks (BNN) against Monte Carlo-dropout (MC-dropout) methods to evaluate their performance and uncertainty scores in two classification tasks with different dataset characteristics.