Gliomas, the most common primary brain tumors, arise from the glial cells and typically exhibit bad prognosis. In particular, glioblastoma (GBM), the most common and aggressive (grade IV) form of brain tumors in adults, presents median survival times of 12 to 15 months. The MONET project will advance current research on the identification and validation of glioma networks across multiple omic layers and molecular biomarkers, aiming at improving patient diagnosis, prognosis, and therapeutic decisions, in a two-fold goal: i) identifying intertumoral multi-omics network-based differences and similarities in different glioma subtypes; and ii) assessing intratumoral heterogeneity in GBM, the most heterogeneous and malignant type of brain cancer, through the identification of distinct and shared singlecell network-based profiles across GBM clones, towards the development of new therapies targeting multiple clones. This will be accomplished through the development of machine learning and bioinformatic tools encompassing network analysis, model regularization, and causal inference, topics at the forefront of machine learning research.