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System-scale network modeling of cancer using EPoC

Tobias Abenius (Institutionen för matematiska vetenskaper, matematisk statistik) ; Rebecka Jörnsten (Institutionen för matematiska vetenskaper, matematisk statistik) ; Teresia Kling ; Linnéa Schmidt ; José Sánchez (Institutionen för matematiska vetenskaper) ; Sven Nelander
Advances in Experimental Medicine and Biology (0065-2598). Vol. 736 (2012), 5, p. 617-643.
[Artikel, refereegranskad vetenskaplig]

One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.

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Denna post skapades 2012-04-19. Senast ändrad 2016-08-19.
CPL Pubid: 156835


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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)
Institutionen för medicin (GU)
Institutionen för matematiska vetenskaperInstitutionen för matematiska vetenskaper (GU)


Matematisk statistik
Bioinformatik och systembiologi
Molekylär medicin
Medicinsk mikrobiologi

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