The complexity of Boolean functions
The complexity of Boolean functions
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
A model for reasoning about persistence and causation
Computational Intelligence
Speech recognition with dynamic Bayesian networks
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An Introduction to Variational Methods for Graphical Models
Machine Learning
Coefficient of determination in nonlinear signal processing
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Mining for Putative Regulatory Elements in the Yeast Genome Using Gene Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Construction of Large-Scale Bayesian Networks by Local to Global Search
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Signal Processing - Special issue: Genomic signal processing
Signal Processing - Special issue: Genomic signal processing
Mappings between probabilistic boolean networks
Signal Processing - Special issue: Genomic signal processing
On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
External Control in Markovian Genetic Regulatory Networks
Machine Learning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Inference of a probabilistic Boolean network from a single observed temporal sequence
EURASIP Journal on Bioinformatics and Systems Biology
Inferring Connectivity of Genetic Regulatory Networks Using Information-Theoretic Criteria
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inference of gene regulatory networks based on a universal minimum description length
EURASIP Journal on Bioinformatics and Systems Biology
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
Automated large-scale control of gene regulatory networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Learning Bayesian networks with integration of indirect prior knowledge
International Journal of Data Mining and Bioinformatics
A knowledge based decision support system for bioinformatics and system biology
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes.