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

ABSTRACT: In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refin

SUBMITTER: Becker AK 

PROVIDER: S-EPMC7906470 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature