System-Scale Network Modeling of Cancer Using EPoC
作者： Tobias Abenius , Rebecka Jörnsten , Teresia Kling , Linnéa Schmidt , José Sánchez
DOI: 10.1007/978-1-4419-7210-1_37
关键词: Cancer 、 Bioinformatics 、 Copy number aberration 、 Computational biology 、 Regression 、 Bootstrapping 、 Lasso (statistics) 、 Bayesian information criterion 、 Medicine 、 Network model 、 Cancer systems biology
摘要: One of the central problems cancer systems biology is to understand complex molecular changes cancerous cells and tissues, use this understanding support development new targeted therapies. EPoC (Endogenous Perturbation analysis Cancer) a network modeling technique for tumor profiles. models are constructed from combined copy number aberration (CNA) mRNA data aim (1) identify genes whose aberrations significantly affect target expression (2) generate markers long- short-term survival patients. Models by combination regression bootstrapping methods. Prognostic scores obtained singular value decomposition networks. We have previously analyzed performance using glioblastoma The Cancer Genome Atlas (TCGA) consortium, shown that resulting contain both known candidate disease-relevant as hubs, well uncover predictors patient survival. Here, we give practical guide how perform in practice R, present set alternative frameworks.
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