  Motivation: The consistent amount of different types of omics data requires
novel methods of analysis and data integration. In this work we describe
Regression2Net, a computational approach to analyse gene expression and
methylation profiles via regression analysis and network-based techniques.
  Results: We identified 284 and 447 unique candidate genes potentially
associated to the Glioblastoma pathology from two networks inferred from mixed
genetic datasets. In-depth biological analysis of these networks reveals genes
that are related to energy metabolism, cell cycle control (AATF), immune system
response and several types of cancer. Importantly, we observed significant
over- representation of cancer related pathways including glioma especially in
the methylation network. This confirms the strong link between methylation and
glioblastomas. Potential glioma suppressor genes ACCN3 and ACCN4 linked to
NBPF1 neuroblastoma breakpoint family have been identified in our expression
network. Numerous ABC transporter genes (ABCA1, ABCB1) present in the
expression network suggest drug resistance of glioblastoma tumors.
