Sebastian Röner

The paper characterizes regulatory variants in human neurons using lentiMPRA and shows that variant effects are influenced by regulatory element activity and sequence context, not population frequency.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

The paper introduces cfDNA UniFlow, a unified preprocessing pipeline for cell-free DNA data, enabling standardized processing and analysis across different settings.

The paper introduces AlphScore, a pathogenicity prediction score for missense variants using AlphaFold2-derived features, showing improved performance when combined with existing scores.