mDBN: motif based learning of gene regulatory networks using dynamic bayesian networks

Authors:
Nizamul Morshed;Madhu Chetty;Nguyen Xuan Vinh;Terry Caelli
Affiliations:
Monash University, Melbourne, Victoria, Australia;Monash University, Melbourne, Victoria, Australia;Monash University, Melbourne, Victoria, Australia;National ICT Australia (NICTA), Melbourne, Victoria, Australia
Venue:
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Year:
2013

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Abstract

Solutions for deriving the most consistent Bayesian gene regulatory network model from given data sets using evolutionary algorithms typically only result in locally optimal solutions. Further, due to genetic drift, merely increasing the size of the population does not overcome this limitation. In this paper, we propose a two-stage genetic algorithm that systematically searches the whole search space using frequent subgraph mining techniques. The approach finds representative patterns present in different local optimal solutions in the first stage and then combines these frequent subgraphs (motifs) in the second stage to converge to the global optima. We apply the algorithm to both synthetic and real life networks of yeast and E.coli and show the effectiveness of our approach.