Survival time prediction of cancer patients

In recent years, in order to reduce noise in experimental data and to add the common role of genes in biological processes into diagnostic and prognostic prediction models, researchers entegrates more than one data type. In this context, many studies have shown that protein interaction networks increase the success of scientific diagnosis. This study aims to find biomarkers that successfully predict the potential survival time of cancer patiens by merging gene transcriptome and protein level data belonging to kidney renal clear cell carcinoma (KIRC) and glioblastoma multiforme (GBM). For this purpose, expression level of mRNA (RNA-seq) and protein (RPPA) data entegrated a with network modelling protein interactions in the human genome. Survival time of patients will be predicted by selecting certain amount of biomarkers and feeding those as inputs to the supervied learning method. For both cancer types, this study showed that our new entegrated method, RPBioNet, outperforms both "only protein" and "only mRNA" methods.