Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Qualitative reasoning about physical systems: a return to roots
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
Reconstructing gene networks from large scale gene expression data
Reconstructing gene networks from large scale gene expression data
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Learning Gene Network Using Conditional Dependence
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Modeling Multiple Time Units Delayed Gene Regulatory Network Using Dynamic Bayesian Network
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Information Sciences: an International Journal
Inference in qualitative probabilistic networks revisited
International Journal of Approximate Reasoning
IEEE Transactions on Information Technology in Biomedicine
Qualitative Motif Detection in Gene Regulatory Networks
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
From qualitative to quantitative probabilistic networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.