From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. T he combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interactio n network.
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research