Title: Identifying disease candidate genes via large-scale gene network analysis

Authors: Haseong Kim; Taesung Park; Erol Gelenbe

Addresses: Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK ' Department of Statistics, Seoul National University, San 56-1, Sillim-dong, Gwanak-gu, Seoul 151-742, South Korea ' Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

Abstract: Gene Regulatory Networks (GRN) provide systematic views of complex living systems, offering reliable and large-scale GRNs to identify disease candidate genes. A reverse engineering technique, Bayesian Model Averaging-based Networks (BMAnet), which ensembles all appropriate linear models to tackle uncertainty in model selection that integrates heterogeneous biological data sets is introduced. Using network evaluation metrics, we compare the networks that are thus identified. The metric 'Random walk with restart (Rwr)' is utilised to search for disease genes. In a simulation our method shows better performance than elastic-net and Gaussian graphical models, but topological quantities vary among the three methods. Using real-data, brain tumour gene expression samples consisting of non-tumour, grade III and grade IV are analysed to estimate networks with a total of 4422 genes. Based on these networks, 169 brain tumour-related candidate genes were identified and some were found to relate to 'wound', 'apoptosis', and 'cell death' processes.

Keywords: large-scale gene regulatory networks; data integration; network comparison; gene identification; disease genes; gene network analysis; bioinformatics; reverse engineering; simulation; brain tumour genes; gene expression; linear models; uncertainty; model selection.

DOI: 10.1504/IJDMB.2014.064014

International Journal of Data Mining and Bioinformatics, 2014 Vol.10 No.2, pp.175 - 188

Received: 23 Apr 2012
Accepted: 28 Apr 2012
Published online: 21 Oct 2014 *

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