Héctor Climente-González

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.

The paper develops a network-guided method for detecting gene-level epistasis in GWAS data, showing it can identify disease-relevant interactions while controlling type I error.

The paper compares six network methods using GWAS data from the GENESIS study to identify breast cancer susceptibility genes, finding that network approaches improve results over standard GWAS.

Martini uses SNP networks to enhance GWAS by identifying functionally related SNPs, improving discovery and interpretability.

The paper introduces block HSIC Lasso, a nonlinear feature selection method for ultra-high dimensional data, showing it retains more biological information than existing techniques in synthetic and real genomic data.