Arno van Hilten

The paper develops methods to detect non-linear genetic interactions using visible neural networks and applies them to identify significant epistasis pairs in inflammatory bowel disease.

This study identifies genetic variants influencing miRNA expression and links them to disease associations using genome-wide association studies and Mendelian randomization.

The paper develops interpretable neural networks for multi-omics data to predict smoking status, age, and LDL levels, showing improved performance and generalizability across cohorts.

GenNet predicts phenotypes from genetic variants using interpretable deep learning, identifying genes and pathways associated with traits like hair color and schizophrenia.