We propose new multistage computational-statistical strategies based on screening-network methods that predict patient survival outcome by screening key survival-related genes [1,2]. In particular, we use: (i) screening approaches to reduce the initial dimension from an high-dimensional space p to a moderate scale d; (ii) Cox-regression model for describing the relationship between patient survival times and predictor variables; (iii) network-penalized Cox-regression approaches to model observed survival time through genome-wide omic profiles while accounting for coordinated functioning of genes in the form of biological pathways or networks.
 Iuliano et al. (2018). Combining pathway identification and breast cancer survival prediction via screening-network methods. Frontiers in genetics, 9.
 Iuliano et al. (2016). Cancer markers selection using network-based Cox regression: A methodological and computational practice. Frontiers in physiology, 7, 208.