Michael Witbrock

This study compares machine learning methods for predicting phenotypes from genetic data, finding that transformers outperform others in simulations and match state-of-the-art models on real data.

This paper introduces SNVformer, a Transformer-based model for GWAS data that predicts gout from SNVs with 83% AUROC, showing promise for scalable genotype-phenotype association analysis.