Tractable Learning and Inference for Large-Scale Probabilistic Boolean Networks
This work addresses the problem of analyzing complex dynamical systems for researchers in computational biology or network science, though it appears incremental as it builds on existing PBN frameworks.
The paper tackles the challenge of learning and inferring large-scale Probabilistic Boolean Networks (PBNs) by introducing the Stochastic Conjunctive Normal Form (SCNF) representation, which enables scalable learning and prediction of long-run dynamic behavior for large systems.
Probabilistic Boolean Networks (PBNs) have been previously proposed so as to gain insights into complex dy- namical systems. However, identification of large networks and of the underlying discrete Markov Chain which describes their temporal evolution, still remains a challenge. In this paper, we introduce an equivalent representation for the PBN, the Stochastic Conjunctive Normal Form (SCNF), which paves the way to a scalable learning algorithm and helps predict long- run dynamic behavior of large-scale systems. Moreover, SCNF allows its efficient sampling so as to statistically infer multi- step transition probabilities which can provide knowledge on the activity levels of individual nodes in the long run.