Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival.In order to find clinically useful prognostic markers for glioma patients' survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. 
Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm
Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival.In order to find clinically useful prognostic markers for glioma patients' survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. 
The Cancer Genome Atlas (TCGA)
Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival.In order to find clinically useful prognostic markers for glioma patients' survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. 
HumanMethylation450 platform
Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival.In order to find clinically useful prognostic markers for glioma patients' survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. 
RNA-seq datasets