Probabilistic Boolean Network

Probabilistic Boolean Networks (PBNs) are mathematical models extending Boolean networks to incorporate uncertainty, primarily used to represent and analyze complex systems with discrete states, such as gene regulatory networks. Current research focuses on efficient PBN construction algorithms, particularly for sparse networks, and on developing robust inference methods, including Bayesian approaches and reinforcement learning techniques, to handle the computational challenges of large-scale networks. These advancements are improving the ability to model and control complex systems in diverse fields, including biology (e.g., cancer treatment) and engineering (e.g., secure control systems), where understanding and manipulating system dynamics under uncertainty is crucial.