Learning gene regulatory networks based on Dempster-Shafer evidence theory
In the post-genomic era, discovering relationships between genes and further constructing gene regulatory networks (GRNs) is an important problem in system biology. In case the GRNs model for a cell is known, we can simulate the gene expression to predict future states of the cell and discover new drugs based on the relationships in the GRNs. In this paper, our aim is to construct a better gene regulatory network. We present a new informative prior over network structure. In the prior, we combine transcription factor (TF) binding data and gene expression data based on Dempster-Shafer (D-S) evidence theory. In addition, a smooth probabilistic model is used in the TF binding data while the Pearson correlation model is used in the gene expression data. We learn the GRNs through dynamic Bayesian network (DBN) inference algorithms. In order to verify the effectiveness of the proposed method, we use the method on the yeast cell cycle gene expression data and also compare the results with those already reported in the literatures. Results obtained from experimental data demonstrate that combing multiple types of data based on D-S evidence theory in modeling GRNs is more accurate than others.