Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk.

摘要: Neuroblastoma is a common cancer in children, affected by number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct single regulatory network topologically not informative enough to explain the complexity neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, personalized networks (idopNetworks) from static gene expression data analyzed 3439 immune 217 high-risk patients 30 low-risk which large patient-specific idopNetworks. By converting these into risk-specific representations, we found shift low high risk or might be due reciprocal change hub regulators. altering directions regulation exerted hubs, it may possible reduce Results holistic, systems-oriented paradigm idopNetworks potentially enable oncologists experimentally identify biomarkers cancers.