Peter Mørch Groth

Kermut is a Gaussian process regression model for protein variant effect prediction that provides uncertainty estimates through its posterior.

The paper curates a benchmark dataset for exploring protein fitness landscapes using wildtype sequences and evaluates different representation paradigms for their predictive power.

This paper evaluates how different performance metrics and generalization definitions affect the assessment of protein regression models.