Daniel Probst

This paper introduces a probabilistic preferential learning model for variant effect prediction, showing that uncertainty-aware strategies improve acquisition performance on single-mutation datasets.

The paper introduces a graph representation that combines static and dynamic protein data, improving performance in three tasks related to protein-ligand interactions.

This paper presents a machine learning approach to predict catalysing enzymes from biochemical reactions using explainable models.