Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Digital Design
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
A framework for path analysis in gene regulatory networks
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
MCMC Based Bayesian Inference for Modeling Gene Networks
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
MCMC Bayesian inference for heart sounds screening in assistive environments
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
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Due to various complexities, as well as noise and high dimensionality, reconstructing a gene regulatory network (GRN) from a high-throughput microarray data becomes computationally intensive.In our earlier work on causal model approach for GRN reconstruction, we had shown the superiority of Markov blanket (MB) algorithm compared to the algorithm using the existing Y and V causal models. In this paper, we show the MB algorithm can be enhanced further by application of the proposed constraint logic minimization (CLM) technique. We describe a framework for minimizing the constraint logic involved (condition independent tests) by exploiting the Markov blanket learning methods developed for a Bayesian network (BN). The constraint relationships are represented in the form of logic using K-map and with the aid of CLM increase the algorithm efficiency and the accuracy. We show improved results by investigations on both the synthetic as well as the real life yeast cell cycle data sets.