Weighted ensemble learning of Bayesian network for gene regulatory networks

Abstract Gene Regulatory Network (GRN) is known as the most adequate representation of genes׳ interactions based on microarray datasets. One of the most performing modeling tools that enable the inference of these networks is a Bayesian network (BN). When preceded by an efficient pre-processing step, BN learning can unveil possible relationships between key disease genes and allows biologists to analyze these interactions and to exploit them. However, the layout of microarray data is different from classic data. This particularity engenders challenges to BN learning in terms of dimensionality and data over-fitting. In this paper, we propose a fuzzy ensemble clustering method that allows outputting small and highly inter-correlated partitions of genes so that we can overcome dimensionality problem. We present a weighted committee based structure algorithm for learning BNs of each partition without over-fitting training dataset. Moreover, we offer an approach for assembling the sub-BNs through genes in common. We also statistically verify and biologically validate our approach.