Andreas Ziegler

Universitätsklinikum Schleswig - Holstein, Kiel, Schleswig-Holstein, Germany

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Publications (607)3151.24 Total impact

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    ABSTRACT: -Dimethylarginines (DMA) interfere with nitric oxide (NO) formation by inhibiting NO synthase (asymmetric dimethylarginine, ADMA) and L-arginine uptake into the cell (ADMA and symmetric dimethylarginine, SDMA). In prospective clinical studies ADMA has been characterized as a cardiovascular risk marker whereas SDMA is a novel marker for renal function and associated with all-cause mortality after ischemic stroke. The aim of the current study was to characterise the environmental and genetic contributions to inter-individual variability of these biomarkers.
    Circulation Cardiovascular Genetics 09/2014; · 6.73 Impact Factor
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    ABSTRACT: Biomarkers are considered as tools to enhance cardiovascular risk estimation. However, the value of biomarkers on risk estimation beyond European risk scores, their comparative impact among different European regions and their role towards personalised medicine remains uncertain. Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) is an European collaborative research project with the primary objective to assess the value of established and emerging biomarkers for cardiovascular risk prediction. BiomarCaRE integrates clinical and epidemiological biomarker research and commercial enterprises throughout Europe to combine innovation in biomarker discovery for cardiovascular disease prediction with consecutive validation of biomarker effectiveness in large, well-defined primary and secondary prevention cohorts including over 300,000 participants from 13 European countries. Results from this study will contribute to improved cardiovascular risk prediction across different European populations. The present publication describes the rationale and design of the BiomarCaRE project.
    European Journal of Epidemiology 09/2014; · 5.12 Impact Factor
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    ABSTRACT: The aim of this study was to determine the impact of functional single nucleotide polymorphism (SNP) pathways involved in the ROS pathway, DNA repair, or TGFB1 signaling on acute or late normal toxicity as well as individual radiosensitivity.
    Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]. 08/2014;
  • Andreas Ziegler, Inke R König
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    ABSTRACT: We review the scope of the scientific discipline genetic epidemiology by considering the steps of genetic epidemiologic research. Starting from the classical definition of genetic epidemiology as provided by Morton and Chung [1978, ISBN-13: 9780125080507], we propose a slightly modernized definition of the term genetic epidemiology.
    Genetic Epidemiology 07/2014; 38(5):379-80. · 4.02 Impact Factor
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    ABSTRACT: Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.
    BMC proceedings 06/2014; 8(1):S1.
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    ABSTRACT: A gastrin-releasing peptide receptor (GRPR) knock-out mouse model provided evidence that the gastrin-releasing peptide (GRP) and its neural circuitry operate as a negative feedback-loop regulating fear, suggesting a novel candidate mechanism contributing to individual differences in fear-conditioning and associated psychiatric disorders such as agoraphobia with/without panic disorder. Studies in humans, however, provided inconclusive evidence on the association of GRP and GRPR variations in agoraphobia with/without panic disorder. Based on these findings, we investigated whether GRP and GRPR variants are associated with agoraphobia. Mental disorders were assessed via the Munich-Composite International Diagnostic Interview (M-CIDI) in 95 patients with agoraphobia with/without panic disorder and 119 controls without any mental disorders. A complete sequence analysis of GRP and GRPR was performed in all participants. We found no association of 16 GRP and 7 GRPR variants with agoraphobia with/without panic disorder.
    Psychiatric Genetics 06/2014; · 2.37 Impact Factor
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    ABSTRACT: Impairment of nerve conduction is common in neurodegenerative and neuroinflammatory diseases such as multiple sclerosis (MS), and measurement of evoked potentials (visual, motor, or sensory) has been widely used for diagnosis and recently also as a prognostic marker for MS. We used a classical genetic approach to identify novel genes controlling nerve conduction. First, we used quantitative trait mapping in F2 progeny of B10/SJL mice to identify EAE31, a locus controlling latency of motor evoked potentials (MEPs) and clinical onset of experimental autoimmune encephalomyelitis. Then, by combining congenic mapping, in silico haplotype analyses, and comparative genomics we identified inositol polyphosphate-4- phosphatase, type II (Inpp4b) as the quantitative trait gene for EAE31. Sequence variants of Inpp4b (C/A, exon 13; A/C, exon 14) were identified as differing among multiple mouse strains and correlated with individual cortical MEP latency differences. To evaluate the functional relevance of the amino acid ex- changes at positions S474R and H548P, we generated transgenic mice carrying the longer-latency allele (Inpp4b474R/548P) in the C57BL/6J background. Inpp4b474R/548P mice exhibited significantly longer cortical MEP latencies (4.5 ` 0.22 ms versus 3.7 ` 0.13 ms; P Z 1.04 10�9), indicating that INPP4B regulates nerve conduction velocity. An association of an INPP4B polymorphism (rs13102150) with MS was observed in German and Spanish MS cohorts (3676 controls and 911 cases) (P Z 8.8 10�3).
    American Journal Of Pathology 05/2014; · 4.60 Impact Factor
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    ABSTRACT: To evaluate the feasibility of hypofractionation with SIB in all settings in Germany to prepare a multicenter treatment comparison. Eligible patients had histopathologically confirmed breast cancer operated by BCS. Patients received WBI 40.0 Gy in 16 fractions of 2.5 Gy. A SIB with 0.5 Gy per fraction was administered to the tumor bed, thereby giving 48.0 Gy in 16 fractions to the boost-PTV sparing heart, LAD, lung, contralateral breast. The primary study objective was feasibility, administration of specified dose in 16 fractions within 22-29 days with adherence to certain dose constraints (heart; LAD; contralateral breast); secondary endpoints were toxicity, QoL. 151 patients were recruited from 7 institutions between 07/11-10/12. 10 patients met exclusion criteria prior to irradiation. All but two patients (99 %) received the prescribed dose in the PTVs. Adherence to dose constraints and time limits was achieved in 89 % (95 % CI 82 % to 93 %). 11 AE were reported in 10 patients; five related to concurrent endocrine therapy. Two of the AEs were related to radiotherapy: grade 3 hot flushes in two cases. QoL remained unchanged. Hypofractionation with a SIB is feasible and was well tolerated in this study.
    Strahlentherapie und Onkologie 04/2014; · 4.16 Impact Factor
  • European Urology Supplements 04/2014; 13(1):e23. · 3.37 Impact Factor
  • Article: In reply.
    Andreas Ziegler, Inke R König
    Deutsches Ärzteblatt International 01/2014; 111(5):68. · 3.54 Impact Factor
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    ABSTRACT: Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi: 10.1002/bimj.201300077).
    Biometrical Journal 01/2014; · 1.15 Impact Factor
  • K Krockenberger, I Bruns, A Ziegler
    DMW - Deutsche Medizinische Wochenschrift 01/2014; · 0.65 Impact Factor
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    ABSTRACT: In current genome-wide association studies (GWAS), the analysis is usually focused on autosomal variants only, and the sex chromosomes are often neglected. Recently, a number of technical hurdles have been described that add to a reluctance of including chromosome X in a GWAS, including complications in genotype calling, imputation, and selection of test statistics. To overcome this, we provide a "how to" guide for analyzing X chromosomal data within a standard GWAS. Following a general pipeline for GWAS, we highlight the steps in which the X chromosome requires specific attention, and we give tentative advice for each of these. Through this, we show that by selection of sensible algorithms and parameter settings, the inclusion of chromosome X in GWAS is manageable. Closing this gap is expected to further elucidate the genetic background of complex diseases, especially of those with sex-specific features.
    Genetic Epidemiology 01/2014; · 4.02 Impact Factor
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    Andreas Ziegler, Inke R. König
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    ABSTRACT: Random Forests are fast, flexible, and represent a robust approach to mining high-dimensional data. They are an extension of classification and regression trees (CART). They perform well even in the presence of a large number of features and a small number of observations. In analogy to CART, random forests can deal with continuous outcome, categorical outcome, and time-to-event outcome with censoring. The tree-building process of random forests implicitly allows for interaction between features and high correlation between features. Approaches are available to measuring variable importance and reducing the number of features. Although random forests perform well in many applications, their theoretical properties are not fully understood. Recently, several articles have provided a better understanding of random forests, and we summarize these findings. We survey different versions of random forests, including random forests for classification, random forests for probability estimation, and random forests for estimating survival data. We discuss the consequences of (1) no selection, (2) random selection, and (3) a combination of deterministic and random selection of features for random forests. Finally, we review a backward elimination and a forward procedure, the determination of trees representing a forest, and the identification of important variables in a random forest. Finally, we provide a brief overview of different areas of application of random forests. WIREs Data Mining Knowl Discov 2014, 4:55–63. doi: 10.1002/widm.1114 For further resources related to this article, please visit the WIREs website.
    Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 01/2014; 4(1). · 1.42 Impact Factor
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    Andreas Ziegler
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    ABSTRACT: Review of [Zbl 1277.62026].
    Biometrical Journal 01/2014; 56(1). · 1.15 Impact Factor
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    ABSTRACT: The advent of next generation sequencing (NGS) technologies enabled the investigation of the rare variant-common disease hypothesis in unrelated individuals, even on the genome-wide level. Analysis of this hypothesis requires tailored statistical methods as single marker tests fail on rare variants. An entire class of statistical methods collapses rare variants from a genomic region of interest (ROI), thereby aggregating rare variants. In an extensive simulation study using data from the Genetic Analysis Workshop 17 we compared the performance of 15 collapsing methods by means of a variety of pre-defined ROIs regarding minor allele frequency thresholds and functionality. Findings of the simulation study were additionally confirmed by a real data set investigating the association between methotrexate clearance and the SLCO1B1 gene in patients with acute lymphoblastic leukemia. Our analyses showed substantially inflated type I error levels for many of the proposed collapsing methods. Only four approaches yielded valid type I errors in all considered scenarios. None of the statistical tests was able to detect true associations over a substantial proportion of replicates in the simulated data. Detailed annotation of functionality of variants is crucial to detect true associations. These findings were confirmed in the analysis of the real data. Recent theoretical work showed that large power is achieved in gene-based analyses only if large sample sizes are available and a substantial proportion of causing rare variants is present in the gene-based analysis. Many of the investigated statistical approaches use permutation requiring high computational cost. There is a clear need for valid, powerful and fast to calculate test statistics for studies investigating rare variants.
    Frontiers in Genetics 01/2014; 5:323.
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    ABSTRACT: Musician's dystonia (MD) affects 1% to 2% of professional musicians and frequently terminates performance careers. It is characterized by loss of voluntary motor control when playing the instrument. Little is known about genetic risk factors, although MD or writer's dystonia (WD) occurs in relatives of 20% of MD patients. We conducted a 2-stage genome-wide association study in whites. Genotypes at 557,620 single-nucleotide polymorphisms (SNPs) passed stringent quality control for 127 patients and 984 controls. Ten SNPs revealed P < 10(-5) and entered the replication phase including 116 MD patients and 125 healthy musicians. A genome-wide significant SNP (P < 5 × 10(-8) ) was also genotyped in 208 German or Dutch WD patients, 1,969 Caucasian, Spanish, and Japanese patients with other forms of focal or segmental dystonia as well as in 2,233 ethnically matched controls. Genome-wide significance with MD was observed for an intronic variant in the arylsulfatase G (ARSG) gene (rs11655081; P = 3.95 × 10(-9) ; odds ratio [OR], 4.33; 95% confidence interval [CI], 2.66-7.05). rs11655081 was also associated with WD (P = 2.78 × 10(-2) ) but not with any other focal or segmental dystonia. The allele frequency of rs11655081 varies substantially between different populations. The population stratification in our sample was modest (λ = 1.07), but the effect size may be overestimated. Using a small but homogenous patient sample, we provide data for a possible association of ARSG with MD. The variant may also contribute to the risk of WD, a form of dystonia that is often found in relatives of MD patients. © 2013 International Parkinson and Movement Disorder Society.
    Movement Disorders 12/2013; · 5.63 Impact Factor
  • M Preuß, A Ziegler
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    ABSTRACT: Background: The random-effects (RE) model is the standard choice for meta-analysis in the presence of heterogeneity, and the standard RE method is the DerSimonian and Laird (DSL) approach, where the degree of heterogeneity is estimated using a moment-estimator. The DSL approach does not take into account the variability of the estimated heterogeneity variance in the estimation of Cochran's Q. Biggerstaff and Jackson derived the exact cumulative distribution function (CDF) of Q to account for the variability of τ^². Objectives: The first objective is to show that the explicit numerical computation of the density function of Cochran's Q is not required. The second objective is to develop an R package with the possibility to easily calculate the classical RE method and the new exact RE method. Methods: The novel approach was validated in extensive simulation studies. The different approaches used in the simulation studies, including the exact weights RE meta-analysis, the I² and τ² estimates together with their confidence intervals were implemented in the R package metaxa. Results: The comparison with the classical DSL method showed that the exact weights RE meta-analysis kept the nominal type I error level better and that it had greater power in case of many small studies and a single large study. The Hedges RE approach had inflated type I error levels. Another advantage of the exact weights RE meta-analysis is that an exact confidence interval for τ² is readily available. The exact weights RE approach had greater power in case of few studies, while the restricted maximum likelihood (REML) approach was superior in case of a large number of studies. Differences between the exact weights RE meta-analysis and the DSL approach were observed in the re-analysis of real data sets. Application of the exact weights RE meta-analysis, REML, and the DSL approach to real data sets showed that conclusions between these methods differed. Conclusions: The simplification does not require the calculation of the density of Cochran's Q, but only the calculation of the cumulative distribution function, while the previous approach required the computation of both the density and the cumulative distribution function. It thus reduces computation time, improves numerical stability, and reduces the approximation error in meta-analysis. The different approaches, including the exact weights RE meta-analysis, the I² and τ² estimates together with their confidence intervals are available in the R package metaxa, which can be used in applications.
    Methods of Information in Medicine 12/2013; 53(1). · 1.08 Impact Factor
  • Andreas Ziegler
    Genetic Epidemiology 12/2013; 37(8). · 4.02 Impact Factor
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    ABSTRACT: The German Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM) states that it uses standardized terms to describe the probabilities of side effects in drug information leaflets. It is unclear, however, whether these terms are actually understood correctly by doctors, pharmacists, and lawyers. A total of 1000 doctors, pharmacists, and lawyers were questioned by mail, and 60.4% of the questionnaires were filled out and returned. In the absence of any particular, potentially suggestive context, the respondents were asked to give a numerical interpretation of each of 20 verbal expressions of probability. Side effects were the subject of a hypothetical physician-patient case scenario. The respondents were also asked to give percentages that they felt corresponded to the terms "common," "uncommon," and "rare." The values obtained were compared with the intended values of the BfArM. The results obtained from the three professional groups resembled each other but stood in marked contrast to the BfArM definitions. With respect to side effects, the pharmacists matched the BfArM definitions most closely (5.8% "common," 1.9% "uncommon" and "rare"), followed by the physicians (3.5%, 0.3%, 0.9%) and the lawyers (0.7%, 0%, 0.7%). When the context of the side effects was not mentioned, the degree of agreement was much lower. Statements about the frequency of side effects are found in all drug information leaflets. Only a small minority of the respondents correctly stated the meaning of terms that are used to describe the frequency of occurrence of side effects, even though they routinely have to convey probabilities of side effects in the course of their professional duties. It can be concluded that the BfArM definitions of these terms do not, in general, correspond to their meanings in ordinary language.
    Deutsches Ärzteblatt International 10/2013; 110(40):669-73. · 3.54 Impact Factor

Publication Stats

17k Citations
3,151.24 Total Impact Points


  • 2005–2014
    • Universitätsklinikum Schleswig - Holstein
      • Institut für Medizinische Biometrie und Statistik (Lübeck)
      Kiel, Schleswig-Holstein, Germany
    • Sapienza University of Rome
      Roma, Latium, Italy
  • 2002–2014
    • Universität zu Lübeck
      • • Institut für Medizinische Biometrie und Statistik
      • • Klinik für Urologie
      • • Institut für Medizinische Informatik
      Lübeck Hansestadt, Schleswig-Holstein, Germany
  • 2010–2013
    • Robert Koch Institut
      Berlín, Berlin, Germany
    • University of Oxford
      • Wellcome Trust Centre for Human Genetics
      Oxford, ENG, United Kingdom
    • University of Michigan
      • Department of Biostatistics
      Ann Arbor, MI, United States
    • Integragen
      Évry-Petit-Bourg, Île-de-France, France
  • 2005–2013
    • University Medical Center Schleswig-Holstein
      Kiel, Schleswig-Holstein, Germany
  • 2012
    • University of Pennsylvania
      • Department of Pharmacology
      Philadelphia, PA, United States
    • University of Greifswald
      • Interfaculty Institute for Genetics and Functional Genomics
      Greifswald, Mecklenburg-Vorpommern, Germany
    • McGill University
      • Department of Epidemiology, Biostatistics and Occupational Health
      Montréal, Quebec, Canada
    • University of Hamburg
      • Department of Radiotherapy and Radio-Oncology
      Hamburg, Hamburg, Germany
  • 2007–2012
    • Bernhard Nocht Institute for Tropical Medicine
      • • Department of Molecular Medicine
      • • Research Group Infectious Disease Epidemiology
      Hamburg, Hamburg, Germany
    • University of Leicester
      • Department of Cardiovascular Sciences
      Leicester, ENG, United Kingdom
    • Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG)
      Köln, North Rhine-Westphalia, Germany
  • 2002–2012
    • Technische Universität Dresden
      Dresden, Saxony, Germany
  • 1997–2012
    • Wellcome Trust Sanger Institute
      Cambridge, England, United Kingdom
    • Universität Trier
      Trier, Rheinland-Pfalz, Germany
  • 2011
    • Medizinische Universität Innsbruck
      • Sektion für Genetische Epidemiologie
      Innsbruck, Tyrol, Austria
    • Indian Statistical Institute
      • Human Genetics Unit (HGU)
      Baranagar, Bengal, India
    • Johannes Gutenberg-Universität Mainz
      • III. Department of Medicine
      Mainz, Rhineland-Palatinate, Germany
    • National Institutes of Health
      • Division of Computational Bioscience
      Bethesda, MD, United States
    • Emory University
      • Department of Epidemiology
      Atlanta, GA, United States
  • 2009–2011
    • Central Institute of Mental Health
      Mannheim, Baden-Württemberg, Germany
    • Pierre and Marie Curie University - Paris 6
      Lutetia Parisorum, Île-de-France, France
    • University of Duisburg-Essen
      • Erwin L. Hahn Institute for Magnetic Resonance Imaging
      Essen, North Rhine-Westphalia, Germany
    • Karl Jaspers Society of North America
      United States
    • University Hospital Frankfurt
      Frankfurt, Hesse, Germany
    • Boston University
      Boston, Massachusetts, United States
  • 2008–2011
    • Justus-Liebig-Universität Gießen
      • Department of Anaesthesiology and Intensive Care Medicine
      Gieben, Hesse, Germany
    • University Medical Center Hamburg - Eppendorf
      • Department of Radiotherapy and Radio-Oncology
      Hamburg, Hamburg, Germany
  • 2004–2011
    • Charité Universitätsmedizin Berlin
      • Institute of Immunogenetics
      Berlin, Land Berlin, Germany
    • Universität Regensburg
      Ratisbon, Bavaria, Germany
  • 1992–2011
    • Freie Universität Berlin
      • • Institute of Chemistry and Biochemistry
      • • Division of Medical Informatics
      Berlín, Berlin, Germany
  • 1990–2009
    • Philipps University of Marburg
      • Klinik für Kinder- und Jugendpsychiatrie und -psychotherapie (Marburg)
      Marburg, Hesse, Germany
  • 1982–2008
    • University of Tuebingen
      • Department of Internal Medicine
      Tübingen, Baden-Württemberg, Germany
  • 2004–2007
    • Georg-August-Universität Göttingen
      Göttingen, Lower Saxony, Germany
  • 1996–2007
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
  • 2006
    • Max Planck Institute for Infection Biology
      • Department of Immunology
      Berlin, Land Berlin, Germany
  • 2003–2004
    • Carl Gustav Carus-Institut
      Pforzheim, Baden-Württemberg, Germany
    • National Cancer Institute (USA)
      Maryland, United States
    • Duke University Medical Center
      • Center for Human Genetics
      Durham, NC, United States
    • Universitätsklinikum Knappschaftskrankenhaus Bochum
      Bochum, North Rhine-Westphalia, Germany
    • RWTH Aachen University
      • Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters
      Aachen, North Rhine-Westphalia, Germany
    • Max-Delbrück-Centrum für Molekulare Medizin
      Berlín, Berlin, Germany
  • 2000
    • Hannover Medical School
      Hanover, Lower Saxony, Germany
  • 1999
    • Ruhr-Universität Bochum
      Bochum, North Rhine-Westphalia, Germany
  • 1998
    • Lilly Deutschland GmbH
      Homburg vor der Höhe, Hesse, Germany
  • 1988–1997
    • Karl-Franzens-Universität Graz
      • Institute of Chemistry
      Graz, Styria, Austria
  • 1994
    • Experimental Pharmacology & Oncology Berlin Buch GmbH
      Berlín, Berlin, Germany
    • Harvard University
      • Department of Chemistry and Chemical Biology
      Cambridge, MA, United States
  • 1993
    • University Hospital München
      München, Bavaria, Germany
  • 1980–1986
    • Universitätsklinikum Tübingen
      Tübingen, Baden-Württemberg, Germany
  • 1983
    • Universitätsklinikum Jena
      Jena, Thuringia, Germany
    • Christian-Albrechts-Universität zu Kiel
      • Institute of Phytopathology
      Kiel, Schleswig-Holstein, Germany