Federico Melograna

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.

The paper introduces NeEDL, a network medicine-based method for detecting higher-order epistatic interactions in complex diseases, showing improved statistical significance and applicability across eight diseases.

netMUG identifies actionable subgroups using network-guided multi-view clustering, showing improved performance over existing methods in synthetic and real data.