Prof. Dr. Christiane Fuchs
Telefon
+49 521 106-4071
- BIGSEM - Bielefeld Graduate School of Economics and Management
- Bielefeld Center for Data Science (BiCDaS)
- Bielefeld Graduate School in Theoretical Sciences
- Center for Uncertainty Studies (CeUS)
- Department Empirische Methoden
- Fakultät für Wirtschaftswissenschaften
- Lehrstuhl für Data Science
- Rektorat -Allgemein-
- Zentrum für Statistik
- Zentrum für interdisziplinäre Forschung
Veröffentlichungen:
Aktuelle Forschungsthemen
- Stochastische Modellierung und statistische Inferenz für dynamische Prozesse, insbesondere Diffusionsprozesse / stochastische Differentialgleichungen
- Unsicherheitsquantifizierung und Lernen aus Unsicherheit
- Computerintensive bayesianische Statistik
- Risikoprädiktion und Integration von Datentypen, zum Beispiel zu Genom, Transkriptom, Klinik und Umwelt
Christiane Fuchs ist Leiterin der Data Science Gruppe an der Fakultät für Wirtschaftswissenschaften der Universität Bielefeld sowie Leiterin der Forschungsgruppe Biostatistik und der Core Facility Statistical Consulting bei Helmholtz Munich.
Sie absolvierte 2003 einen MSc in Computational Modelling an der Brunel University West London und 2005 ein Diplom in Mathematik mit Informatik an der Universität Hannover. Anschließend begann sie ihre Forschungslaufbahn mit Themen der bayesianischen Inferenz und promovierte 2010 in Statistik an der Ludwig-Maximilians-Universität München. Seit 2011 arbeitet sie bei Helmholtz Munich, zunächst als wissenschaftliche Mitarbeiterin im Bereich biologische Modellierung am Institut für Bioinformatik und Systembiologie, dann als Team- und Gruppenleiterin der Biostatistik am Institut für Computational Biology. 2017 gründete sie die Core Facility Statistical Consulting bei Helmholtz Munich, eine wissenschaftliche Serviceeinheit für Lehre und statistische Beratung für Forschungsinstitute, Universitäten und Industriepartner. 2018 wechselte sie als Professorin für Data Science an die Universität Bielefeld.
Christiane Fuchs forscht sowohl methoden- als auch anwendungsorientiert zu verschiedenen interdisziplinären Themen, die von den Wirtschaftswissenschaften über die mathematische Epidemiologie bis hin zur Medizin reichen. Sie ist oder war Principal Investigator in mehreren nationalen und internationalen Forschungsprojekten, die unter Anderem von der DFG, dem BMBF, dem BMG, den NIH, der EU und der Helmholtz-Gemeinschaft gefördert werden.
Seit Oktober 2023 ist Christiane Fuchs Prorektorin für Forschung und Forschungsvernetzung der Universität Bielefeld.
- International Society for Bayesian Analysis
- Deutsche Region der Internationalen Biometrischen Gesellschaft
- Deutsche Statistische Gesellschaft (DStatG)
- Deutsche Mathematiker Vereinigung (DMV), Fachgruppe Stochastik
- Single-Cell Network Germany
- Forschungsverbund BioMedizin Bielefeld (FBMB)
- Helmholtz AI Associate
- Deutscher Hochschulverband
- Open Science Center, LMU München
Projektleitung
Projektmitglied
-
2025; Verbundprojekt: Gemeinschaftliche Optimierung der Versuchsplanung durch Daten- und Fachwissenschaften, Teilvorhaben: Statistische Inferenz und experimentelles Design; Fuchs, Christiane; Bund
-
2024; Uncertainty Quantification - From Data to Reliable Knowledge; Fuchs, Christiane; sonstige Private
-
2024; Uncertainty Quantification - From Data to Reliable Knowledge; Fuchs, Christiane; sonstige Private
-
2024; Prospective COVID-19 Cohort Munich; Fuchs, Christiane;
-
2023; Chronische Schmerzen bei Patient*innen mit und ohne entzündlich rheumatische Erkrankung in der Primär- und Sekundärversorgung: transsektorale Bestandsaufnahme, Überprüfung einer neuen Überweisungsstrategie und Analyse von Kontextfaktoren; Fuchs, Christiane;
Projektmitglied
-
2025; Economic Policy in Complex Environments; Dawid, Herbert; Europäische Union
-
2024; Entwicklung eines KI-basierten Entscheidungsunterstützungssystems zur individualisierten Vorhersage wirksamer Antibiotikatherapien; Cimiano, Philipp; Bund
Als Autor*in
- Wille K, Deventer E, Sadjadian P, et al. Arterial and Venous Thromboembolic Complications in 832 Patients with BCR-ABL-Negative Myeloproliferative Neoplasms. Hämostaseologie . 2023.
- Röchter M, Thiesbrummel S, Marchi H, Fuchs C, Rudwaleit M. Changes in diagnosing giant cell arteritis of the temporal artery over a 7.7 year period. Annals of the Rheumatic Diseases. 2023;82(1579).
- Marchi H, Fuchs C. Hurdle-QAP models overcome dependency and sparsity in scientific collaboration count networks. The Journal of Mathematical Sociology. 2023:1-28.
- Contento L, Castelletti N, Raimúndez E, et al. Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates. Epidemics. 2023;43: 100681.
- Wille K, Brouka M, Bernhardt J, et al. Outcome of 129 Pregnancies in Polycythemia Vera Patients: A Report of the European LeukemiaNET. HemaSphere. 2023;7(5): e882.
- Pedron S, Laxy M, Radon K, et al. Socioeconomic and risk-related drivers of compliance with measures to prevent SARS-CoV-2 infection: evidence from the Munich-based KoCo19 study. BMC Public Health. 2023;23(1): 860.
- Reinkemeyer C, Khazaei Y, Weigert M, et al. The Prospective COVID-19 Post-Immunization Serological Cohort in Munich (KoCo-Impf): Risk Factors and Determinants of Immune Response in Healthcare Workers. Viruses. 2023;15(7): 1574.
- Le Gleut R, Plank M, Pütz P, et al. The representative COVID-19 cohort Munich (KoCo19): from the beginning of the pandemic to the Delta virus variant. BMC Infectious Diseases. 2023;23(1): 466.
- Wille K, Huenerbein K, Jagenberg E, et al. Bleeding complications in bcr-abl negative myeloproliferative neoplasms (MPN): a retrospective single-center study of 829 MPN patients. European Journal of Haematology. 2022;108(2):154-162.
- Pieschner S, Hasenauer J, Fuchs C. Identifiability analysis for models of the translation kinetics after mRNA transfection. Journal of Mathematical Biology. 2022;84(7): 56.
- Puchinger K, Castelletti N, Rubio-Acero R, et al. The interplay of viral loads, clinical presentation, and serological responses in SARS-CoV-2 – Results from a prospective cohort of outpatient COVID-19 cases. Virology. 2022;569:37-43.
- Beyerl J, Rubio-Acero R, Castelletti N, et al. A dried blood spot protocol for high throughput analysis of SARS-CoV-2 serology based on the Roche Elecsys anti-N assay. EBioMedicine. 2021;70: 103502.
- Olbrich L, Castelletti N, Schälte Y, et al. A Serology Strategy for Epidemiological Studies Based on the Comparison of the Performance of Seven Different Test Systems - The Representative COVID-19 Cohort Munich. bioRxiv. 2021.
- Krautenbacher N, Kabesch M, Horak E, et al. Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors. Pediatric allergy and immunology . 2021;32(2):295-304.
- Brand I, Gilberg L, Bruger J, et al. Broad T Cell Targeting of Structural Proteins After SARS-CoV-2 Infection: High Throughput Assessment of T Cell Reactivity Using an Automated Interferon Gamma Release Assay. Frontiers in Immunology. 2021;12.
- Brand I, Gilberg L, Bruger J, et al. Broad T Cell Targeting of Structural Proteins After SARS-CoV-2 Infection: High Throughput Assessment of T Cell Reactivity Using an Automated Interferon Gamma Release Assay. Frontiers in Immunology. 2021;12: 688436.
- Radon K, Bakuli A, Pütz P, et al. From first to second wave: follow-up of the prospective Covid-19 cohort (KoCo19) in Munich (Germany). BMC Infectious Diseases. 2021;21(1): 925.
- Olbrich L, Castelletti N, Schälte Y, et al. Head-to-head evaluation of seven different seroassays including direct viral neutralisation in a representative cohort for SARS-CoV-2. Journal of General Virology. 2021;102(10).
- Pritsch M, Radon K, Bakuli A, et al. Prevalence and Risk Factors of Infection in the Representative COVID-19 Cohort Munich. International journal of environmental research and public health. 2021;18(7): 3572.
- Stegelmann F, Wille K, Marchi H, et al. Publisher Correction: Significant association of cutaneous adverse events with hydroxyurea: results from a prospective non-interventional study in BCR-ABL1-negative myeloproliferative neoplasms (MPN) - on behalf of the German Study Group-MPN. Leukemia. 2021;35:3635.
- Sekhri R, Sadjadian P, Becker T, et al. Ruxolitinib-treated polycythemia vera patients and their risk of secondary malignancies. Annals of Hematology. 2021;100(11):2707-2716.
- Stegelmann F, Wille K, Busen H, et al. Significant association of cutaneous adverse events with hydroxyurea: results from a prospective non-interventional study in BCR-ABL1-negative myeloproliferative neoplasms (MPN) - on behalf of the German Study Group-MPN. Leukemia. 2021;35:628-631.
- Rubio-Acero R, Beyerl J, Muenchhoff M, et al. Spatially resolved qualified sewage spot sampling to track SARS-CoV-2 dynamics in Munich - One year of experience. Science of The Total Environment. 2021;797: 149031.
- Amrhein L, Fuchs C. stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R. BMC Bioinformatics. 2021;22: 123.
- Fuetterer C, Augustin T, Fuchs C. Adapted single-cell consensus clustering (adaSC3). Advances in Data Analysis and Classification volume. 2020;14(4):885–896.
- Pieschner S, Fuchs C. Bayesian inference for diffusion processes: using higher-order approximations for transition densities. Royal Society Open Science. 2020;7(10): 200270.
- Becker M, Noll-Puchta H, Amend D, et al. CLUE: a bioinformatic and wet-lab pipeline for multiplexed cloning of custom sgRNA libraries. Nucleic acids research. 2020;48(13).
- Marchi H, Fuchs C. Modelling the impact of spatial proximity on scientific collaboration networks. In: Proceedings of the 35th International Workshop on Statistical Modelling (IWSM) 2020. 2020.
- Radon K, Saathoff E, Pritsch M, et al. Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19). BMC Public Health. 2020;20(1): 1036.
- Amrhein L, Fuchs C. Stochastic profiling of mRNA counts using HMC. In: Proceedings of the 35th International Workshop on Statistical Modelling (IWSM) 2020. 2020.
- Amrhein L, Harsha K, Fuchs C. A mechanistic model for the negative binomial distribution of single-cell mRNA counts. bioRxiv. 2019.
- Hibbah EH, El Maroufy H, Fuchs C, Ziad T. An MCMC computational approach for a continuous time state-dependent regime switching diffusion process. JOURNAL OF APPLIED STATISTICS. 2019;47:1354-1374.
- Krautenbacher N, Flach N, Bock A, et al. A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological and environmental factors. Allergy. 2019;74(7):1364-1373.
- Wille K, Stegelmann F, Busen H, et al. Cutaneous adverse events (CAE) in MPN patients with cytoreductive therapy are strongly associated with hydroxyurea (HU): results from a prospective non-interventional study. In: ONCOLOGY RESEARCH AND TREATMENT. Vol 42. Basel: Karger; 2019: 96.
- Wille K, Sadjadian P, Becker T, et al. High risk of recurrent venous thromboembolism in BCR-ABL-negative myeloproliferative neoplasms after termination of anticoagulation. ANNALS OF HEMATOLOGY. 2019;98(1):93-100.
- Wille K, Sadjadian P, Becker T, Kolatzki V, Fuchs C, Griesshammer M. No evidence for Ruxolitinib treatment being a potential risk factor for secondary primary malignancies (SPM) in 201 BCR/ABL-negative myeloproliferative neoplasm patients. In: ONCOLOGY RESEARCH AND TREATMENT. Vol 42. Basel: Karger; 2019: 71.
- El Omari M, El Maroufy H, Fuchs C. Non parametric estimation for fractional diffusion processes with random effects. STATISTICS. 2019;53(4):753-769.
- Tirier SM, Park J, Preusser F, et al. Pheno-seq - linking visual features and gene expression in 3D cell culture systems. Scientific reports. 2019;9(1): 12367.
- Neschen S, Wu M, Fuchs C, et al. Impact of Brain Fatty Acid Signaling on Peripheral Insulin Action in Mice. Experimental and Clinical Endocrinology & Diabetes. 2018;128(01):20-29.
- Seyednasrollah F, Koestler DC, Wang T, et al. A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer patients. JCO Clinical Cancer Informatics. 2017;1(1):1-15.
- Krautenbacher N, Theis FJ, Fuchs C. Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies. Computational and Mathematical Methods in Medicine. 2017;2017: 7847531.
- Dirmeier S, Fuchs C, Mueller NS, Theis FJ. netReg: network-regularized linear models for biological association studies. Bioinformatics. 2017;34(5):896-898.
- Köferle A, Worf K, Breunig C, et al. CORALINA. A universal method for the generation of gRNA libraries for CRISPR-based screening. BMC Genomics. 2016;17(1): 917.
- Kondofersky I, Theis FJ, Fuchs C. Inferring catalysis in biological systems. IET Systems Biology. 2016;10(6):210-218.
- Guinney J, Wang T, Laajala TD, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer. Development of a prognostic model through a crowdsourced challenge with open clinical trial data. The Lancet Oncology. 2016;18(1):132-142.
- Kondofersky I, Laimighofer M, Kurz C, et al. Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration. F1000Research. 2016;5: 2671.
- Sadic D, Schmidt K, Groh S, et al. Atrx promotes heterochromatin formation at retrotransposons. EMBO reports. 2015;16(7):836-850.
- Illner K, Fuchs C, Theis FJ. Bayesian blind source separation applied to the lymphocyte pathway. In: Gilli M, ed. 21st International Conference on Computational Statistics (COMPSTAT 2014). Proceedings of COMPSTAT 2014. Red Hook, NJ; 2015: 625-632.
- Kondofersky I, Fuchs C, Theis FJ. Identifying latent dynamic components in biological systems. IET Systems Biology. 2015;9(5):193-203.
- Kaufmann S, Fuchs C, Gonik M, Khrameeva EE, Mironov AA, Frishman D. Inter-Chromosomal Contact Networks Provide Insights into Mammalian Chromatin Organization. PLOS ONE. 2015;10(5): e0126125.
- Illner K, Miettinen J, Fuchs C, et al. Model selection using limiting distributions of second-order blind source separation algorithms. Signal Processing. 2015;113:95-103.
- Illner K, Fuchs C, Theis FJ. Bayesian Blind Source Separation for Data with Network Structure. Journal of Computational Biology. 2014;21(11):855-865.
- Illner K, Fuchs C, Theis FJ. Einzelzellanalysen. Biologie in Hochauflösung. GIT Laborportal. 20.10.2014.
- Fuchs C. Mische und herrsche. Zelluläre Genexpression statistisch unterstützt analysieren. Laborpraxis. 2014;2014(5):20-22.
- Bajikar SS, Fuchs C, Roller A, Theis FJ, Janes KA. Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles. Proceedings of the National Academy of Sciences. 2014;111(5):E626-E635.
- Illner K, Fuchs C, Theis FJ. Single-cell analysis. Biology at high resolution. Separation - Science & Applications. 2014;34:24-26.
- Schneider K, Fuchs C, Dobay A, et al. Dissection of cell cycle–dependent dynamics of Dnmt1 by FRAP and diffusion-coupled modeling. Nucleic Acids Research. 2013;41(9):4860-4876.
- Fuchs C. Inference for Diffusion Processes. With Applications in Life Sciences. Berlin, Heidelberg: Springer; 2013.
- Albrecht E, Waldenberger M, Krumsiek J, et al. Metabolite profiling reveals new insights into the regulation of serum urate in humans. Metabolomics. 2013;10(1):141-151.
- Wahl S, Holzapfel C, Yu Z, et al. Metabolomics reveals determinants of weight loss during lifestyle intervention in obese children. Metabolomics. 2013;9(6):1157-1167.
- Illner K, Fuchs C, Theis FJ. Blind source separation using latent Gaussian graphical models. In: Larjo A, Schober S, Farhan M, Bossert M, Yli-Harja O, Tampere International Center for Signal Processing., eds. Ninth International Workshop on Computational Systems Biology, WCSB 2012. Vol TICSP series. Tampere, Finland; 2012: 43-50.
- Treu G, Fuchs F, Dargatz C. Implicit Authorization for Social Location Disclosure. Journal of Software. 2008;3(1):18-26.
- Treu G, Fuchs F, Dargatz C. Implicit Authorization for Accessing Location Data in a Social Context. In: The Second International Conference on Availability, Reliability and Security (ARES'07). Piscataway, NJ: IEEE; 2007: 263-272.
- Dargatz C. A Diffusion Approximation for an Epidemic Model. Sonderforschungsbereich 386, Discussion Paper. Vol 517. München: Ludwig-Maximilians-Universität; 2006.
- Dargatz C, Georgescu V, Held L. Stochastic Modelling of the Spatial Spread of Influenza in Germany. Austrian Journal of Statistics. 2005;35(1):5-20.
Publikationsliste
Publikationsliste (CSV)
Publikationsliste (PDF) im "AMA"-Zitierstil
Publikationsliste (PDF) im "APA (6th ed.)"-Zitierstil
Publikationsliste (PDF) im "Chicago"-Zitierstil
Publikationsliste (PDF) im "Frontiers"-Zitierstil
Publikationsliste (PDF) im "Harvard"-Zitierstil
Publikationsliste (PDF) im "IEEE"-Zitierstil
Publikationsliste (PDF) im "LNCS"-Zitierstil
Publikationsliste (PDF) im "MLA"-Zitierstil
Download
RDF/XML-Format
JSON-LD-Format
Turtle-Format
N3-Format
Christiane Fuchs ist Leiterin der Data Science Gruppe an der Fakultät für Wirtschaftswissenschaften der Universität Bielefeld sowie Leiterin der Forschungsgruppe Biostatistik und der Core Facility Statistical Consulting bei Helmholtz Munich.
Sie absolvierte 2003 einen MSc in Computational Modelling an der Brunel University West London und 2005 ein Diplom in Mathematik mit Informatik an der Universität Hannover. Anschließend begann sie ihre Forschungslaufbahn mit Themen der bayesianischen Inferenz und promovierte 2010 in Statistik an der Ludwig-Maximilians-Universität München. Seit 2011 arbeitet sie bei Helmholtz Munich, zunächst als wissenschaftliche Mitarbeiterin im Bereich biologische Modellierung am Institut für Bioinformatik und Systembiologie, dann als Team- und Gruppenleiterin der Biostatistik am Institut für Computational Biology. 2017 gründete sie die Core Facility Statistical Consulting bei Helmholtz Munich, eine wissenschaftliche Serviceeinheit für Lehre und statistische Beratung für Forschungsinstitute, Universitäten und Industriepartner. 2018 wechselte sie als Professorin für Data Science an die Universität Bielefeld.
Christiane Fuchs forscht sowohl methoden- als auch anwendungsorientiert zu verschiedenen interdisziplinären Themen, die von den Wirtschaftswissenschaften über die mathematische Epidemiologie bis hin zur Medizin reichen. Sie ist oder war Principal Investigator in mehreren nationalen und internationalen Forschungsprojekten, die unter Anderem von der DFG, dem BMBF, dem BMG, den NIH, der EU und der Helmholtz-Gemeinschaft gefördert werden.
Seit Oktober 2023 ist Christiane Fuchs Prorektorin für Forschung und Forschungsvernetzung der Universität Bielefeld.
- International Society for Bayesian Analysis
- Deutsche Region der Internationalen Biometrischen Gesellschaft
- Deutsche Statistische Gesellschaft (DStatG)
- Deutsche Mathematiker Vereinigung (DMV), Fachgruppe Stochastik
- Single-Cell Network Germany
- Forschungsverbund BioMedizin Bielefeld (FBMB)
- Helmholtz AI Associate
- Deutscher Hochschulverband
- Open Science Center, LMU München
Als Projektleitung
Als wiss. Mitarbeiter*in
-
2025; Verbundprojekt: Gemeinschaftliche Optimierung der Versuchsplanung durch Daten- und Fachwissenschaften, Teilvorhaben: Statistische Inferenz und experimentelles Design; Fuchs, Christiane; Bund
-
2024; Uncertainty Quantification - From Data to Reliable Knowledge; Fuchs, Christiane; sonstige Private
-
2024; Uncertainty Quantification - From Data to Reliable Knowledge; Fuchs, Christiane; sonstige Private
-
2024; Prospective COVID-19 Cohort Munich; Fuchs, Christiane;
-
2023; Chronische Schmerzen bei Patient*innen mit und ohne entzündlich rheumatische Erkrankung in der Primär- und Sekundärversorgung: transsektorale Bestandsaufnahme, Überprüfung einer neuen Überweisungsstrategie und Analyse von Kontextfaktoren; Fuchs, Christiane;
Als wiss. Mitarbeiter*in
-
2025; Economic Policy in Complex Environments; Dawid, Herbert; Europäische Union
-
2024; Entwicklung eines KI-basierten Entscheidungsunterstützungssystems zur individualisierten Vorhersage wirksamer Antibiotikatherapien; Cimiano, Philipp; Bund
Als Autor*in:
- Wille K, Deventer E, Sadjadian P, et al. Arterial and Venous Thromboembolic Complications in 832 Patients with BCR-ABL-Negative Myeloproliferative Neoplasms. Hämostaseologie . 2023.
- Röchter M, Thiesbrummel S, Marchi H, Fuchs C, Rudwaleit M. Changes in diagnosing giant cell arteritis of the temporal artery over a 7.7 year period. Annals of the Rheumatic Diseases. 2023;82(1579).
- Marchi H, Fuchs C. Hurdle-QAP models overcome dependency and sparsity in scientific collaboration count networks. The Journal of Mathematical Sociology. 2023:1-28.
- Contento L, Castelletti N, Raimúndez E, et al. Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates. Epidemics. 2023;43: 100681.
- Wille K, Brouka M, Bernhardt J, et al. Outcome of 129 Pregnancies in Polycythemia Vera Patients: A Report of the European LeukemiaNET. HemaSphere. 2023;7(5): e882.
- Pedron S, Laxy M, Radon K, et al. Socioeconomic and risk-related drivers of compliance with measures to prevent SARS-CoV-2 infection: evidence from the Munich-based KoCo19 study. BMC Public Health. 2023;23(1): 860.
- Reinkemeyer C, Khazaei Y, Weigert M, et al. The Prospective COVID-19 Post-Immunization Serological Cohort in Munich (KoCo-Impf): Risk Factors and Determinants of Immune Response in Healthcare Workers. Viruses. 2023;15(7): 1574.
- Le Gleut R, Plank M, Pütz P, et al. The representative COVID-19 cohort Munich (KoCo19): from the beginning of the pandemic to the Delta virus variant. BMC Infectious Diseases. 2023;23(1): 466.
- Wille K, Huenerbein K, Jagenberg E, et al. Bleeding complications in bcr-abl negative myeloproliferative neoplasms (MPN): a retrospective single-center study of 829 MPN patients. European Journal of Haematology. 2022;108(2):154-162.
- Pieschner S, Hasenauer J, Fuchs C. Identifiability analysis for models of the translation kinetics after mRNA transfection. Journal of Mathematical Biology. 2022;84(7): 56.
- Puchinger K, Castelletti N, Rubio-Acero R, et al. The interplay of viral loads, clinical presentation, and serological responses in SARS-CoV-2 – Results from a prospective cohort of outpatient COVID-19 cases. Virology. 2022;569:37-43.
- Beyerl J, Rubio-Acero R, Castelletti N, et al. A dried blood spot protocol for high throughput analysis of SARS-CoV-2 serology based on the Roche Elecsys anti-N assay. EBioMedicine. 2021;70: 103502.
- Olbrich L, Castelletti N, Schälte Y, et al. A Serology Strategy for Epidemiological Studies Based on the Comparison of the Performance of Seven Different Test Systems - The Representative COVID-19 Cohort Munich. bioRxiv. 2021.
- Krautenbacher N, Kabesch M, Horak E, et al. Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors. Pediatric allergy and immunology . 2021;32(2):295-304.
- Brand I, Gilberg L, Bruger J, et al. Broad T Cell Targeting of Structural Proteins After SARS-CoV-2 Infection: High Throughput Assessment of T Cell Reactivity Using an Automated Interferon Gamma Release Assay. Frontiers in Immunology. 2021;12.
- Brand I, Gilberg L, Bruger J, et al. Broad T Cell Targeting of Structural Proteins After SARS-CoV-2 Infection: High Throughput Assessment of T Cell Reactivity Using an Automated Interferon Gamma Release Assay. Frontiers in Immunology. 2021;12: 688436.
- Radon K, Bakuli A, Pütz P, et al. From first to second wave: follow-up of the prospective Covid-19 cohort (KoCo19) in Munich (Germany). BMC Infectious Diseases. 2021;21(1): 925.
- Olbrich L, Castelletti N, Schälte Y, et al. Head-to-head evaluation of seven different seroassays including direct viral neutralisation in a representative cohort for SARS-CoV-2. Journal of General Virology. 2021;102(10).
- Pritsch M, Radon K, Bakuli A, et al. Prevalence and Risk Factors of Infection in the Representative COVID-19 Cohort Munich. International journal of environmental research and public health. 2021;18(7): 3572.
- Stegelmann F, Wille K, Marchi H, et al. Publisher Correction: Significant association of cutaneous adverse events with hydroxyurea: results from a prospective non-interventional study in BCR-ABL1-negative myeloproliferative neoplasms (MPN) - on behalf of the German Study Group-MPN. Leukemia. 2021;35:3635.
- Sekhri R, Sadjadian P, Becker T, et al. Ruxolitinib-treated polycythemia vera patients and their risk of secondary malignancies. Annals of Hematology. 2021;100(11):2707-2716.
- Stegelmann F, Wille K, Busen H, et al. Significant association of cutaneous adverse events with hydroxyurea: results from a prospective non-interventional study in BCR-ABL1-negative myeloproliferative neoplasms (MPN) - on behalf of the German Study Group-MPN. Leukemia. 2021;35:628-631.
- Rubio-Acero R, Beyerl J, Muenchhoff M, et al. Spatially resolved qualified sewage spot sampling to track SARS-CoV-2 dynamics in Munich - One year of experience. Science of The Total Environment. 2021;797: 149031.
- Amrhein L, Fuchs C. stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R. BMC Bioinformatics. 2021;22: 123.
- Fuetterer C, Augustin T, Fuchs C. Adapted single-cell consensus clustering (adaSC3). Advances in Data Analysis and Classification volume. 2020;14(4):885–896.
- Pieschner S, Fuchs C. Bayesian inference for diffusion processes: using higher-order approximations for transition densities. Royal Society Open Science. 2020;7(10): 200270.
- Becker M, Noll-Puchta H, Amend D, et al. CLUE: a bioinformatic and wet-lab pipeline for multiplexed cloning of custom sgRNA libraries. Nucleic acids research. 2020;48(13).
- Marchi H, Fuchs C. Modelling the impact of spatial proximity on scientific collaboration networks. In: Proceedings of the 35th International Workshop on Statistical Modelling (IWSM) 2020. 2020.
- Radon K, Saathoff E, Pritsch M, et al. Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19). BMC Public Health. 2020;20(1): 1036.
- Amrhein L, Fuchs C. Stochastic profiling of mRNA counts using HMC. In: Proceedings of the 35th International Workshop on Statistical Modelling (IWSM) 2020. 2020.
- Amrhein L, Harsha K, Fuchs C. A mechanistic model for the negative binomial distribution of single-cell mRNA counts. bioRxiv. 2019.
- Hibbah EH, El Maroufy H, Fuchs C, Ziad T. An MCMC computational approach for a continuous time state-dependent regime switching diffusion process. JOURNAL OF APPLIED STATISTICS. 2019;47:1354-1374.
- Krautenbacher N, Flach N, Bock A, et al. A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological and environmental factors. Allergy. 2019;74(7):1364-1373.
- Wille K, Stegelmann F, Busen H, et al. Cutaneous adverse events (CAE) in MPN patients with cytoreductive therapy are strongly associated with hydroxyurea (HU): results from a prospective non-interventional study. In: ONCOLOGY RESEARCH AND TREATMENT. Vol 42. Basel: Karger; 2019: 96.
- Wille K, Sadjadian P, Becker T, et al. High risk of recurrent venous thromboembolism in BCR-ABL-negative myeloproliferative neoplasms after termination of anticoagulation. ANNALS OF HEMATOLOGY. 2019;98(1):93-100.
- Wille K, Sadjadian P, Becker T, Kolatzki V, Fuchs C, Griesshammer M. No evidence for Ruxolitinib treatment being a potential risk factor for secondary primary malignancies (SPM) in 201 BCR/ABL-negative myeloproliferative neoplasm patients. In: ONCOLOGY RESEARCH AND TREATMENT. Vol 42. Basel: Karger; 2019: 71.
- El Omari M, El Maroufy H, Fuchs C. Non parametric estimation for fractional diffusion processes with random effects. STATISTICS. 2019;53(4):753-769.
- Tirier SM, Park J, Preusser F, et al. Pheno-seq - linking visual features and gene expression in 3D cell culture systems. Scientific reports. 2019;9(1): 12367.
- Neschen S, Wu M, Fuchs C, et al. Impact of Brain Fatty Acid Signaling on Peripheral Insulin Action in Mice. Experimental and Clinical Endocrinology & Diabetes. 2018;128(01):20-29.
- Seyednasrollah F, Koestler DC, Wang T, et al. A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer patients. JCO Clinical Cancer Informatics. 2017;1(1):1-15.
- Krautenbacher N, Theis FJ, Fuchs C. Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies. Computational and Mathematical Methods in Medicine. 2017;2017: 7847531.
- Dirmeier S, Fuchs C, Mueller NS, Theis FJ. netReg: network-regularized linear models for biological association studies. Bioinformatics. 2017;34(5):896-898.
- Köferle A, Worf K, Breunig C, et al. CORALINA. A universal method for the generation of gRNA libraries for CRISPR-based screening. BMC Genomics. 2016;17(1): 917.
- Kondofersky I, Theis FJ, Fuchs C. Inferring catalysis in biological systems. IET Systems Biology. 2016;10(6):210-218.
- Guinney J, Wang T, Laajala TD, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer. Development of a prognostic model through a crowdsourced challenge with open clinical trial data. The Lancet Oncology. 2016;18(1):132-142.
- Kondofersky I, Laimighofer M, Kurz C, et al. Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration. F1000Research. 2016;5: 2671.
- Sadic D, Schmidt K, Groh S, et al. Atrx promotes heterochromatin formation at retrotransposons. EMBO reports. 2015;16(7):836-850.
- Illner K, Fuchs C, Theis FJ. Bayesian blind source separation applied to the lymphocyte pathway. In: Gilli M, ed. 21st International Conference on Computational Statistics (COMPSTAT 2014). Proceedings of COMPSTAT 2014. Red Hook, NJ; 2015: 625-632.
- Kondofersky I, Fuchs C, Theis FJ. Identifying latent dynamic components in biological systems. IET Systems Biology. 2015;9(5):193-203.
- Kaufmann S, Fuchs C, Gonik M, Khrameeva EE, Mironov AA, Frishman D. Inter-Chromosomal Contact Networks Provide Insights into Mammalian Chromatin Organization. PLOS ONE. 2015;10(5): e0126125.
- Illner K, Miettinen J, Fuchs C, et al. Model selection using limiting distributions of second-order blind source separation algorithms. Signal Processing. 2015;113:95-103.
- Illner K, Fuchs C, Theis FJ. Bayesian Blind Source Separation for Data with Network Structure. Journal of Computational Biology. 2014;21(11):855-865.
- Illner K, Fuchs C, Theis FJ. Einzelzellanalysen. Biologie in Hochauflösung. GIT Laborportal. 20.10.2014.
- Fuchs C. Mische und herrsche. Zelluläre Genexpression statistisch unterstützt analysieren. Laborpraxis. 2014;2014(5):20-22.
- Bajikar SS, Fuchs C, Roller A, Theis FJ, Janes KA. Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles. Proceedings of the National Academy of Sciences. 2014;111(5):E626-E635.
- Illner K, Fuchs C, Theis FJ. Single-cell analysis. Biology at high resolution. Separation - Science & Applications. 2014;34:24-26.
- Schneider K, Fuchs C, Dobay A, et al. Dissection of cell cycle–dependent dynamics of Dnmt1 by FRAP and diffusion-coupled modeling. Nucleic Acids Research. 2013;41(9):4860-4876.
- Fuchs C. Inference for Diffusion Processes. With Applications in Life Sciences. Berlin, Heidelberg: Springer; 2013.
- Albrecht E, Waldenberger M, Krumsiek J, et al. Metabolite profiling reveals new insights into the regulation of serum urate in humans. Metabolomics. 2013;10(1):141-151.
- Wahl S, Holzapfel C, Yu Z, et al. Metabolomics reveals determinants of weight loss during lifestyle intervention in obese children. Metabolomics. 2013;9(6):1157-1167.
- Illner K, Fuchs C, Theis FJ. Blind source separation using latent Gaussian graphical models. In: Larjo A, Schober S, Farhan M, Bossert M, Yli-Harja O, Tampere International Center for Signal Processing., eds. Ninth International Workshop on Computational Systems Biology, WCSB 2012. Vol TICSP series. Tampere, Finland; 2012: 43-50.
- Treu G, Fuchs F, Dargatz C. Implicit Authorization for Social Location Disclosure. Journal of Software. 2008;3(1):18-26.
- Treu G, Fuchs F, Dargatz C. Implicit Authorization for Accessing Location Data in a Social Context. In: The Second International Conference on Availability, Reliability and Security (ARES'07). Piscataway, NJ: IEEE; 2007: 263-272.
- Dargatz C. A Diffusion Approximation for an Epidemic Model. Sonderforschungsbereich 386, Discussion Paper. Vol 517. München: Ludwig-Maximilians-Universität; 2006.
- Dargatz C, Georgescu V, Held L. Stochastic Modelling of the Spatial Spread of Influenza in Germany. Austrian Journal of Statistics. 2005;35(1):5-20.
Publikationsliste
Publikationsliste (CSV)
Publikationsliste (PDF) im "AMA"-Zitierstil
Publikationsliste (PDF) im "APA (6th ed.)"-Zitierstil
Publikationsliste (PDF) im "Chicago"-Zitierstil
Publikationsliste (PDF) im "Frontiers"-Zitierstil
Publikationsliste (PDF) im "Harvard"-Zitierstil
Publikationsliste (PDF) im "IEEE"-Zitierstil
Publikationsliste (PDF) im "LNCS"-Zitierstil
Publikationsliste (PDF) im "MLA"-Zitierstil
Download
RDF/XML-Format
JSON-LD-Format
Turtle-Format
N3-Format