Piotr Szul

The paper identifies an epistatic interaction between RBMS3 and CDKN2B-AS1 in CAD risk using VariantSpark and BitEpi, with validation in UK Biobank and All of Us.

This study identifies novel Alzheimer's disease genes and epistasis using machine learning GWAS tools, capturing more phenotypic variance and discovering new loci with epistatic interactions.

RFlocalfdr thresholds variable importance from Random Forests using an empirical Bayes approach, achieving high accuracy and speed for high-dimensional datasets.

TRIBES detects genomic relatedness up to 7th degree relatives and identifies disease susceptibility loci using a single pipeline.

This paper introduces CursedForest, a Random Forest implementation that scales to millions of features and outperforms existing methods in speed.