Identifying disease candidate genes via large-scale gene network analysis. (1st January 2014)

• Record Type: Journal Article
• Authors: Kim, Haseong; Park, Taesung; Gelenbe, Erol
• Is Part Of: International journal of data mining and bioinformatics. Volume 10:Number 2(2014)
• Page Start: 175
• Page End: 188
• Publication Date: 2014-01-01
• Subjects: large-scale gene regulatory networks -- data integration -- network comparison -- candidate gene identification; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.