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3 Publications visible to you, out of a total of 3
From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach.

Abstract (Expand)

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 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.

Authors: A. K. Becker, M. Dorr, S. B. Felix, F. Frost, H. J. Grabe, M. M. Lerch, M. Nauck, U. Volker, H. Volzke, L. Kaderali

Date Published: 13th Feb 2021

Publication Type: Journal

Identifying Differentially Expressed MicroRNAs, Target Genes, and Key Pathways Deregulated in Patients with Liver Diseases.

Abstract (Expand)

Liver diseases are important causes of morbidity and mortality worldwide. The aim of this study was to identify differentially expressed microRNAs (miRNAs), target genes, and key pathways as innovative … diagnostic biomarkers in liver patients with different pathology and functional state. We determined, using RT-qPCR, the expression of 472 miRNAs in 125 explanted livers from subjects with six different liver pathologies and from control livers. ANOVA was employed to obtain differentially expressed miRNAs (DEMs), and miRDB (MicroRNA target prediction database) was used to predict target genes. A miRNA-gene differential regulatory (MGDR) network was constructed for each condition. Key miRNAs were detected using topological analysis. Enrichment analysis for DEMs was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We identified important DEMs common and specific to the different patient groups and disease progression stages. hsa-miR-1275 was universally downregulated regardless the disease etiology and stage, while hsa-let-7a*, hsa-miR-195, hsa-miR-374, and hsa-miR-378 were deregulated. The most significantly enriched pathways of target genes controlled by these miRNAs comprise p53 tumor suppressor protein (TP53)-regulated metabolic genes, and those involved in regulation of methyl-CpG-binding protein 2 (MECP2) expression, phosphatase and tensin homolog (PTEN) messenger RNA (mRNA) translation and copper homeostasis. Our findings show a novel panel of deregulated miRNAs in the liver tissue from patients with different liver pathologies. These miRNAs hold potential as biomarkers for diagnosis and staging of liver diseases.

Authors: M. Gholizadeh, S. Szelag-Pieniek, M. Post, M. Kurzawski, J. Prieto, J. Argemi, M. Drozdzik, L. Kaderali

Date Published: 6th Oct 2020

Publication Type: Journal

Whither systems medicine?

Abstract (Expand)

New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary … approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.

Authors: R. Apweiler, T. Beissbarth, M. R. Berthold, N. Bluthgen, Y. Burmeister, O. Dammann, A. Deutsch, F. Feuerhake, A. Franke, J. Hasenauer, S. Hoffmann, T. Hofer, P. L. Jansen, L. Kaderali, U. Klingmuller, I. Koch, O. Kohlbacher, L. Kuepfer, F. Lammert, D. Maier, N. Pfeifer, N. Radde, M. Rehm, I. Roeder, J. Saez-Rodriguez, U. Sax, B. Schmeck, A. Schuppert, B. Seilheimer, F. J. Theis, J. Vera, O. Wolkenhauer

Date Published: 3rd Mar 2018

Publication Type: Not specified

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