Abstract
BackgroundBayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables. They have been applied in various biological contexts, including gene regulatory networks and protein–protein interactions inference. Generally, learning Bayesian networks from experimental data is NP-hard, leading to widespread use of heuristic search methods giving suboptimal results. However, in cases when the acyclicity of the graph can be externally ensured, it is possible to find the optimal network in polynomial time. While our previously developed tool BNFinder implements polynomial time algorithm, reconstructing networks with the large amount of experimental data still leads to computations on single CPU growing exceedingly.ResultsIn the present paper we propose parallelized algorithm designed for multi-core and distributed systems and its implementation in the improved version of BNFinder—tool for learning optimal Bayesian networks. The new algorithm has been tested on different simulated and experimental datasets showing that it has much better efficiency of parallelization than the previous version. BNFinder gives comparable results in terms of accuracy with respect to current state-of-the-art inference methods, giving significant advantage in cases when external information such as regulators list or prior edge probability can be introduced, particularly for datasets with static gene expression observations.ConclusionsWe show that the new method can be used to reconstruct networks in the size range of thousands of genes making it practically applicable to whole genome datasets of prokaryotic systems and large components of eukaryotic genomes. Our benchmarking results on realistic datasets indicate that the tool should be useful to a wide audience of researchers interested in discovering dependencies in their large-scale transcriptomic datasets.
Highlights
Bayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables
The graph is pre-determined by external constraints. The latter is true when dealing with dynamic Bayesian networks (BNs) or when user defines the regulation hierarchy restricting the set of possible edges in case of static BNs
We studied the effect of different parameters by plotting Area Under the Precision Recall curve (AUPR) against Area Under Receiver Operating Characteristic curve (AUROC) values
Summary
Bayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables. Bayesian networks (BNs) are graphical representations of multivariate joint probability distributions factorized consistently with the dependency structure among variables This often gives concise structures that are easy to interpret even for non-specialists. The copyright holder for this preprint It is made available under the graph is pre-determined by external constraints The latter is true when dealing with dynamic BNs or when user defines the regulation hierarchy restricting the set of possible edges in case of static BNs. The latter is true when dealing with dynamic BNs or when user defines the regulation hierarchy restricting the set of possible edges in case of static BNs This algorithm was implemented in BNFinder - a tool for BNs reconstruction from experimental data [4]
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