Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks

Abstract

Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.

Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm

“…2021 [27] Large-scale Dynamic Causal Mode (PEB) 2020 [28] Latent Factor Causal Models (LFCMs) 2022 [29] Recurrent Generative Adversarial Network (RGAN) 2021 [21] Truncated Matrix Power Iteration (TMPI) 2022 [16] Deep Reinforcement Learning (DRL) 2022 [19] BN with Pruning Strategies (CO-CDG) 2022 [15] Amortization Transformer (AT-EC) 2023 [30] Deconfounded Functional Structure Estimation (DeFuSE) 2023 [31] 2.1.1. Causal Brain Networks Causal brain networks consist of multiple brain nodes and causal interactions between different nodes, and accurate learning of causal brain networks is valuable for understanding the functioning of brain cognition and gaining insight into the pathogenesis of brain diseases [32,33].…”

“…etc. Recently, Wei et al [ 15 ] proposed a method for learning CBNs based on BN with pruning strategies. Zhang et al [ 16 ] proposed a CBNs learning method based on truncated matrix power iteration.…”