Network-Based Variable Selection for Survival Outcomes in Oncological Data

Carrasquinha, Eunice; Veríssimo, André; Lopes, Marta B.; Vinga, Susana

doi:10.1007/978-3-030-45385-5_49

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other strategies for accounting for network-based information during the learning process encompass the introduction of weights in the coefficients, which can be derived either from the network topology of external databases (e.g., protein-protein interaction databases) or of the data network structure itself, e.g., the correlation/covariance matrices, as shown in survival and classification on breast, melanoma, and ovarian cancer datasets [ 42 , 43 , 119 , 120 , 121 ], using available implementations in the R packages [ 122 ] and [ 42 ] ( Table 1 ). Considering the elastic net regularizer, this is equivalent to adding a weight factor to the penalty term.…”

Section: Network Discovery In Glioblastomamentioning

confidence: 99%

The Role of Network Science in Glioblastoma

Lopes

Martins

Vinga

et al. 2021

Cancers

Self Cite

View full text Add to dashboard Cite

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.

show abstract