Published in Arxiv, 2020
Survival month for non-small lung cancer patients depend upon which stage of lung cancer is present. Our aim is to identify smoking specific gene expression biomarkers in prognosis of lung cancer patients. In this paper, we introduce the network elastic net, a generalization of network lasso that allows for simultaneous clustering and regression on graphs. In network elastic net, we consider similar patients based on smoking cigarettes per year to form the network. We then further find the suitable cluster among patients based on coefficients of genes having different survival month structures and showed the efficacy of the clusters using stage enrichment. This can be used to identify the stage of cancer using gene expression and smoking behavior of patients without doing any tests.
Recommended citation: Barnwal, Avinash et al. “Survival regression with accelerated failure time model in XGBoost.” ArXiv abs/2006.04920 (2020): n. pag. https://arxiv.org/pdf/2006.04920.pdf