Abstract
We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map.
Highlights
With the completion of the human genome project and successful sequencing genomes of many other organisms, emphasis of postgenomic research has been shifted to the understanding of functions of genes [1]
Precision corresponds to the expected success rate in the experimental validation of the predicted interactions and it is calculated as TP/(TP +FP), where TP is the number of true positives and FP is the number of false positives
We investigated the dynamic Bayesian networks (DBNs) modeling of cell cycle gene regulatory networks (GRNs) and the variational Bayesian structural EM (VBSEM) learning of network topology
Summary
With the completion of the human genome project and successful sequencing genomes of many other organisms, emphasis of postgenomic research has been shifted to the understanding of functions of genes [1]. We investigate in this paper reverse engineering gene regulatory networks (GRNs) based on time-series microarray data. GRNs are the functioning circuitry in living organisms at the gene level. They display the regulatory relationships among genes in a cellular system. These regulatory relationships are involved directly and indirectly in controlling the production of protein and in mediating metabolic processes. Understanding GRNs can provide new ideas for treating complex diseases and breakthroughs for designing new drugs
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