Distributed Local Bayesian Network for Gene Regulatory Network Reconstruction

Abstract

Gene regulatory network provides an effective way to study functional genomes. Reconstruction of gene regulatory network can better help us to reveal the regulatory relationship and complex mechanism between genes, thus exploring the essence of biological systems and lives. At present, the modeling methods of gene regulatory network either solve the problem of accuracy or scale of gene regulatory network by optimizing the model or combining various models. However, with the continuous increase of network scale and the computational complexity, the reconstruction of the network has exceeded the ability of single machine. Thus, we presented the Distributed Local Bayesian Network (D-LBN) method based on MapReduce framework to solve this problem. D-LBN is based on LBN algorithm which uses mutual information method, k-nearest neighbor algorithm, Bayesian network model and conditional mutual information method to reconstruct gene regulatory network. D-LBN realizes distributed computing using MapReduce framework at the step of regulatory direction learning with Bayesian network model to achieve the purpose of reconstructing large-scale gene regulatory network. Experiments based on two scales of dataset is done to verify the performance of D-LBN in reconstructing gene regulatory network at different scales. The results show that D-LBN is consistent with LBN in evaluation index and much more efficient than LBN on large-scale network reconstruction. And the scale of the network that can be reconstructed is also greatly improved.