The Role of Network Science in Glioblastoma
Abstract
:Simple Summary
Abstract
1. Molecular Networks in Precision Oncology
2. Network Discovery in Glioblastoma
2.1. Differential Network Analysis
2.2. Gene Coexpression Module Detection
2.3. Trans-Omics Network Discovery
2.4. Network-Based Learning
2.4.1. Cancer Subtype Identification
2.4.2. Model-Based Biomarker Discovery
2.5. Causal Discovery
3. Major Challenges and Future Strategies
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
Banjo | Bayesian network inference with Java objects |
BN | Bayesian network |
CE | Community extraction |
CLR | Context-likelihood-relatedness |
CNA | Copy number aberration |
CSPRV | Cancer subtype prediction using |
DAG | Directed acyclic graph |
DEG | Differential expressed gene |
DiME | Disease module extraction |
DINGO | Differential network analysis in genomics |
EMT | Epithelial-to-mesenchymal transition |
EPoC | Endogenous perturbation analysis of cancer |
GBM | Glioblastoma multiforme |
GBM-BioDP | Glioblastoma Bio Discovery Portal |
GEO | Gene Expression Omnibus |
GMM | Gaussian mixture model |
GO | Gene Ontology |
GWAS | Genome-wide association study |
HGG | High-grade glioma |
IAMB | Incremental associated Markov blanket |
IDA | Intervention calculus when the DAG is absent |
iDINGO | Integrative differential network analysis in genomics |
InTRINSiC | Integrative Modeling of Transcription Regulatory Interactions for |
Systematic Inference of Susceptibility in Cancer | |
KEGG | Kyoto Encyclopedia of Genes and Genomes |
KINC | Knowledge Independent Network Construction |
LGG | Low-grade glioma |
TCGA | The Cancer Genome Atlas |
CCLE | Cancer Cell Line Encyclopedia |
CGGA | Chinese Glioma Genome Atlas |
EN | Elastic net |
lasso | Least absolute shrinkage selection operator |
lncRNA | Long non-coding RNA |
MI | Mutual information |
MSLCRN | Module-specific lncRNA-mRNA causal regulatory networks |
NGS | Next-generation sequencing |
PDX | Patient-derived xenograft |
RNA-seq | RNA sequencing |
scRNA-seq | Single-cell RNA sequencing |
SNF | Similarity Network Fusion |
SNP | Single-nucleotide polymorphism |
SYGNAL | Systems Genetics Network Analysis |
TAM | Tumor-associated macrophage |
TCI | Tumor-specific causal inference |
TF | Transcriptional factor |
TRN | Transcriptional regulatory network |
twiner | Twin networks recovery |
WGCNA | Weighted gene coexpression network analysis |
WSNF | Weighted similarity network fusion |
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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
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 StyleLopes, 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
APA StyleLopes, M. B., Martins, E. P., Vinga, S., & Costa, B. M. (2021). The Role of Network Science in Glioblastoma. Cancers, 13(5), 1045. https://doi.org/10.3390/cancers13051045