Florian R. Hölzlwimmer

The paper uses fibroblast RNA-seq to improve molecular diagnosis in dystonia patients by identifying RNA expression and splicing defects missed by genomic sequencing.

FuncRVP improves rare variant polygenic risk scores by integrating functional gene embeddings, enhancing performance for traits with higher heritability.

The paper develops AbExp, a model predicting aberrant gene expression across tissues, achieving 12% average precision by integrating variant effects and tissue-specific expression variability.

This paper introduces DeepRVAT, a deep learning model for rare variant association genetics, showing improved gene discovery and risk prediction using whole-exome sequencing data from UK Biobank.

The paper predicts tissue-specific aberrant splicing by improving DNA-based models with RNA-seq data and benchmarking over 8.8 million variants across 49 human tissues.