Timothy R. Bergquist

This study calibrates in-frame indel variant effect predictors for clinical classification, showing they reach multiple evidence levels but perform worse than missense predictors.

This paper improves variant effect predictors by calibrating them gene- and domain-aware, increasing evidence assignment and accuracy for variant classification.

The paper calibrates three new computational tools for clinical variant classification, finding they provide evidence strength comparable to previously recommended tools.

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

The paper assesses rare missense variants in genome sequences using PP3/BP4 evidence strength classifications and finds few variants meet strong or moderate criteria.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.