This article is part of the supplement: Selected articles from the 9th Annual Biotechnology and Bioinformatics Symposium (BIOT 2012)
Integrative network-based Bayesian analysis of diverse genomics data
1 Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, USA
2 Department of Statistics, University of Oxford, Oxford, UK
3 MRC Harwell, Oxon, UK
BMC Bioinformatics 2013, 14(Suppl 13):S8 doi:10.1186/1471-2105-14-S13-S8Published: 1 October 2013
In order to better understand cancer as a complex disease with multiple genetic and epigenetic factors, it is vital to model the fundamental biological relationships among these alterations as well as their relationships with important clinical outcomes.
We develop an
We show that the iNET approach has greater power in identifying cancer-related microRNAs than non-integrative approaches based on realistic simulated datasets. In the TCGA GBM study, we found many mRNA-microRNA pairs and microRNAs that are associated with patient survival time, with some of these associations identified in previous studies.
The iNET discovers relationships consistent with the underlying biological mechanisms among these variables, as well as identifying important biomarkers that are potentially relevant to patient survival. In addition, we identified some microRNAs that can potentially affect patient survival which are missed by non-integrative approaches.