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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 refinement of such group Bayesian networks to account for a specific target variable, like a disease. The 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 interaction network.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008735","type":"journal-article","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T21:59:25Z","timestamp":1613167165000},"page":"e1008735","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1906-0583","authenticated-orcid":true,"given":"Ann-Kristin","family":"Becker","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7471-475X","authenticated-orcid":true,"given":"Marcus","family":"D\u00f6rr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0744-091X","authenticated-orcid":true,"given":"Stephan B.","family":"Felix","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Frost","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans J.","family":"Grabe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9643-8263","authenticated-orcid":true,"given":"Markus M.","family":"Lerch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Nauck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5689-3448","authenticated-orcid":true,"given":"Uwe","family":"V\u00f6lker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry","family":"V\u00f6lzke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2359-2294","authenticated-orcid":true,"given":"Lars","family":"Kaderali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,2,12]]},"reference":[{"key":"pcbi.1008735.ref001","article-title":"Inferring cellular networks\u2014A review","author":"F Markowetz","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"pcbi.1008735.ref002","article-title":"Fault detection and pathway analysis using a dynamic Bayesian network","author":"MT Amin","year":"2019","journal-title":"Chemical Engineering Science"},{"key":"pcbi.1008735.ref003","article-title":"Inferring gene regulatory networks from expression data","author":"L Kaderali","year":"2008","journal-title":"Studies in Computational Intelligence"},{"key":"pcbi.1008735.ref004","article-title":"Inference of Gene Regulatory Network Based on Local Bayesian Networks","author":"F Liu","year":"2016","journal-title":"PLoS Computational Biology"},{"key":"pcbi.1008735.ref005","article-title":"Learning discrete Bayesian networks from continuous data","author":"YC Chen","year":"2017","journal-title":"Journal of Artificial Intelligence Research"},{"key":"pcbi.1008735.ref006","article-title":"Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network","author":"S Lakho","year":"2017","journal-title":"Sukkur IBA Journal of Computing and Mathematical Sciences"},{"issue":"1","key":"pcbi.1008735.ref007","article-title":"A review of Bayesian networks and structure learning","volume":"40","author":"TJ Koski","year":"2012","journal-title":"Mathematica Applicanda"},{"key":"pcbi.1008735.ref008","volume-title":"Probabilistic graphical models: principles and techniques","author":"D Koller","year":"2009"},{"key":"pcbi.1008735.ref009","article-title":"Comparative analysis of discretization methods in Bayesian networks","author":"A F Nojavan","year":"2017","journal-title":"Environmental Modelling and Software"},{"key":"pcbi.1008735.ref010","doi-asserted-by":"crossref","unstructured":"Sturlaugson LE, Sheppard JW. 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