Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Simulating, Analyzing, and Animating Dynamical Systems: A Guide Toi Xppaut for Researchers and Students
Regulatory network reconstruction using stochastic logical networks
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
Learning bayesian networks does not have to be NP-Hard
MFCS'06 Proceedings of the 31st international conference on Mathematical Foundations of Computer Science
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We present a novel algorithm for reconstructing the topology of regulatory networks based on the Stochastic Logical Network model. Our method, by avoiding the computation of the Markov model parameters is able to reconstruct the topology of the SLN model in polynomial time instead of exponential as in previous study [29]. To test the performance of the method, we apply it to different datasets (both synthetic and experimental) covering the expression of several cell cycle regulators which have been thoroughly studied [18,11]. We compare the results of our method with the popular Dynamic Bayesian Network approach in order to quantify the ability to reconstruct true dependencies. Although both methods able to recover only a part of the true dependencies from realistic data, our method gives consistently better results than Dynamic Bayesian Networks in terms of the number of correctly reconstructed edges, sensitivity and statistical significance.