Andreas Henschel

This study benchmarks machine learning, deep learning, and polygenic risk score tools on 80 binary phenotypes from openSNP data to evaluate their predictive performance.

The paper reassesses pathogenicity of variants in inherited retinal diseases using population-scale genomics in the UAE, identifying clinically relevant variants and highlighting discrepancies between clinical and population-based classifications.

This paper introduces MVIB, a deep learning model that combines gut microbiome data with metabolomic data to predict disease, achieving high performance across multiple disease cohorts.