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Review

The Role of Network Science in Glioblastoma

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Center for Mathematics and Applications (CMA), FCT, UNL, 2829-516 Caparica, Portugal
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NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, 2829-516 Caparica, Portugal
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Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
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ICVS/3B’s—PT Government Associate Laboratory, 4710-057/4805-017 Braga/Guimarães, Portugal
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INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisbon, Portugal
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IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
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Author to whom correspondence should be addressed.
Academic Editors: Giuseppe Lombardi, Emilie Le Rhun, Ahmed Idbaih, Matthias Preusser and Pim French
Cancers 2021, 13(5), 1045; https://doi.org/10.3390/cancers13051045
Received: 4 February 2021 / Revised: 19 February 2021 / Accepted: 22 February 2021 / Published: 2 March 2021
(This article belongs to the Special Issue Recurrent Glioblastoma)
Knowledge extraction from cancer genomic studies is continuously challenged by the fast-growing technological advances generating high-dimensional data. Network science is a promising discipline to cope with the resulting complex and heterogeneous datasets, enabling the disclosure of the molecular networks involved in cancer development and progression. We present a narrative review of the network-based strategies that have been applied to glioblastoma (GBM), a complex and heterogeneous disease, along with a discussion on the relevant findings and open challenges and future research opportunities.
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. View Full-Text
Keywords: network analysis; differential network expression; model regularization; causal discovery; multi-omics; biomarker selection; precision medicine; personalized therapy network analysis; differential network expression; model regularization; causal discovery; multi-omics; biomarker selection; precision medicine; personalized therapy
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MDPI and ACS Style

Lopes, M.B.; Martins, E.P.; Vinga, S.; Costa, B.M. The Role of Network Science in Glioblastoma. Cancers 2021, 13, 1045. https://doi.org/10.3390/cancers13051045

AMA Style

Lopes MB, Martins EP, Vinga S, Costa BM. The Role of Network Science in Glioblastoma. Cancers. 2021; 13(5):1045. https://doi.org/10.3390/cancers13051045

Chicago/Turabian Style

Lopes, Marta B., Eduarda P. Martins, Susana Vinga, and Bruno M. Costa. 2021. "The Role of Network Science in Glioblastoma" Cancers 13, no. 5: 1045. https://doi.org/10.3390/cancers13051045

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