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The Role of Network Science in Glioblastoma.

Marta B LopesEduarda P MartinsSusana VingaBruno M Costa
Published in: Cancers (2021)
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.
Keyphrases
  • single cell
  • public health
  • small molecule
  • high throughput
  • rna seq
  • healthcare
  • machine learning
  • young adults
  • network analysis
  • deep learning