The role of network science in glioblastoma
Funding Information: This work was partially supported by national funds through Funda??o para a Ci?ncia e a Tecnologia (FCT) with references CEECINST/00102/2018, CEECIND/00072/2018 and PD/BDE/143154/2019, UIDB/04516/2020, UIDB/00297/2020, UIDB/50021/2020, UIDB/50022/2020, UIDB/50026/2020, UIDP/50026/2020, NORTE-01-0145-FEDER-000013, and NORTE-01-0145-FEDER-000023 and projects PTDC/CCI-BIO/4180/2020 and DSAIPA/DS/0026/2019. This project has received funding from the European Union?s Horizon 2020 research and innovation program under Grant Agreement No. 951970 (OLISSIPO project). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. ; Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine. ; publishersversion ; published