Integration of multiple datasets grants in-silico investigations with higher statistical and reasoning power to elucidate secondary discoveries hidden to the initial data producers. Here we introduce a novel method for the network analysis of messenger RNA regulation. Post-translational regulation of gene activity by microRNA molecules is investigated, combining expression data and sequence binding predictions. A set of sounding machine learning techniques allows the integration of these structural and functional results, conveying them into an ensemble graph of regulations. Ensemble graphs are embedded in Bayesian network classifiers following an ascending order of complexity and finally evaluated by their goodness-of-fit and classification performances. The new proposal is put to the test in the integration of four Alzheimer's disease datasets, reaching optimal values of 94.39% ± 2.34 accuracy and 0.9794 ± 0.01 for the area under the ROC curve. Detected regulations within the optimal network structure match the state-of-the-art literature. Additional dependences suggest previously unreported regulations in Alzheimer's disease research.

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