Dynamic Bayesian Network Modeling Based on Structure Prediction for Gene Regulatory Network

Abstract

Gene regulatory network can intuitively reflect the interaction between genes, and an indepth study of these relationships plays a significant role in the treatment and prevention of clinical diseases. Therefore, correct reconstruction of gene regulatory network has become the first critical step in the study of disease treatment and prevention at the genetic level. Among the methods for gene regulatory network reconstruction, the Bayesian network model has been widely concerned because of its advantages of expressing both the regulatory relationship and the degree of strength between genes. Nevertheless, the complexity of the Bayesian network model in structure learning is extremely high, making the efficiency of the reconstruction network is low and the scale is limited. Therefore, this paper proposed a dynamic Bayesian network modeling based on structure prediction (DBN-SP). The method combines the correlation model with the dynamic Bayesian network model. On the premise of making full use of the search strategy of dynamic Bayesian network model structure learning, the candidate parent node set is selected based on the structure prediction method firstly. Based on this, in the process of structure learning, some redundant information can be removed and the search space can be reduced. After the network reconstructed, structure optimization by using the conditional mutual information method can further remove redundant edges and make the network more accurate. The experimental results show that DBN-SP greatly improves the efficiency and scale of the gene regulatory network reconstruction, and the accuracy and other indexes are also improved. DBN-SP is freely accessible at https://github.com/quluxuan/DBN-SP.git.

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

“…Furthermore, DBNs have a broad range of engineering applications, such as managing transcriptional regulatory relationships between cancer genes 3 , identifying connectivity issues between human brain regions through high-order DBNs using functional magnetic resonance imaging time series data 4 , and analyzing the vascularization in the formation process of engineered tissues, aiming to enhance the accuracy of predicting future time steps and ensuring an acceptable uncertainty in forecasting the future progress of the organization 5 . Integrating structural prediction methods, such as mutual information and maximum information coefficient into the DBN model enhances the efficiency and scale of gene regulatory network reconstruction 6 .…”

Dynamic Bayesian Network Structure Learning Based on an Improved Bacterial Foraging Optimization Algorithm

“…Furthermore, DBNs have a broad range of engineering applications, such as managing transcriptional regulatory relationships between cancer genes 3 , identifying connectivity issues between human brain regions through high-order DBNs using functional magnetic resonance imaging time series data 4 , and analyzing the vascularization in the formation process of engineered tissues, aiming to enhance the accuracy of predicting future time steps and ensuring an acceptable uncertainty in forecasting the future progress of the organization 5 . Integrating structural prediction methods, such as mutual information and maximum information coefficient into the DBN model enhances the efficiency and scale of gene regulatory network reconstruction 6 . However, the introduction of time information into the DBN network increases search space complexity, reduces structure learning accuracy, and impedes the direct application of the static BN learning method.…”