Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive mixtures of local experts
Neural Computation
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We are concerned with the problem of inferring genetic regulatory networks from a collection of temporal observations. This is often done via estimating a Dynamic Bayesian Network (DBN) from time series of gene expression data. However, when applying this algorithm to the limited quantities of experimental data that nowadays technologies can provide, its estimation is not robust. We introduce a weak learners' methodology for this inference problem, study few methods to produce Weak Dynamic Bayesian Networks (WDBNs), and demonstrate its advantages on simulated gene expression data.