Vittorio Fortino

This paper introduces MODAE+, a multi-objective domain adaptation framework that improves drug response prediction across cancer cell lines and patient tumours by preserving biological structure and heterogeneity.

This paper introduces a network-based method to estimate therapeutic efficacy and adverse reaction potential of anti-cancer drug combinations using gene set enrichment analysis and network propagation.

This paper introduces MODAE, a deep learning method that integrates cell-line and patient omics data to predict cancer survival and treatment response, preserving tumor heterogeneity.

This study integrates public transcriptomic data with network analysis to identify molecular features of psoriasis, revealing key genes and networks involved in the disease.

The paper compares missing data handling methods for variant pathogenicity predictors and finds that simpler imputation methods like mean imputation perform best.

eUTOPIA provides an intuitive R Shiny application for preprocessing and analyzing microarray data without requiring computational expertise.