Veröffentlichungen 2020

  • M.R. Berthold, C. Borgelt, F. Höppner, F. Klawonn, R. Silipo: Guide to Intelligent Data Science: How to Intelligently Make Use of Real Data. Springer, London (2020)
  • M. Frentrup, Z. Zhou, M. Steglich, J.P. Meier-Kolthoff, M. Göker, T. Riedel, B. Bunk, C. Spröer, J. Overmann, M. Blaschitz, A. Indra, L. von Müller, T.A. Kohl, S. Niemann, C. Seyboldt, F. Klawonn, N. Kumar, T.D. Lawley, S. Garcia-Fernandez, R. Canton, R. del Campo, O. Zimmermann, U. Groß, M. Achtman, U. Nübel: A publicly accessible database for Clostridioides difficile genome sequences supports tracing of transmission chains and epidemics. Microbial Genomics (2020), doi: 10.1099/mgen.0.000410
  • L. Kühnle, U. Mücke, W.M. Lechner, F. Klawonn, L. Grigull: A Social Network for people without a diagnosis: Sketching, prototyping and evaluation of RarePairs. Journal of Medical Internet Research 22(9):e21849, doi: 10.2196/21849
  • J. Palm, G. Hoffmann, F. Klawonn, O. Tutarel, H. Palm, S. Holdenrieder, P. Ewert: Continuous, complete and comparable NT-proBNP reference ranges in healthy children. Clinical Chemistry and Laboratory Medicine 58(9), (2020), 1509-1516, doi: 10.1515/cclm-2019-1185
  • C. Slabik, M. Kalbarczyk S. Danisch, R. Zeidler, F. Klawonn, V. Volk, N. Krönke, F. Feuerhake, C. Ferreira de Figueiredo, R. Blasczyk, H. Olbrich, S.J. Theobald, A. Schneider, A. Ganser, C. von Kaisenberg, S. Lienenklaus, A. Bleich, W. Hammerschmidt, R. Stripecke: CAR-T cells targeting Epstein-Barr virus gp350 validated in a humanized mouse model of EBV infection and lymphoproliferative disease. Molecular Therapy - Oncolytics 18 (2020), 504-524
  • S.J. Theobald, C. Kreer, S. Khailaie, A. Bonifacius, B. Eiz-Vesper, C. Figueiredo, M. Mach, M. Backovic, M. Ballmaier, J. Koenig, H. Olbrich, A. Schneider, V. Volk, S. Danisch, L. Gieselmann, M.S. Ercanoglu, M. Messerle, C. von Kaisenberg, T. Witte, F. Klawonn, M. Meyer-Hermann, F. Klein, R. Stripecke: Repertoire characterization and validation of gB-specific human IgGs directly cloned from humanized mice vaccinated with dendritic cells and protected against HCMV. Plos Pathogens (2020), doi:10.1371/journal.ppat.1008560
  • L.S. de Araujo, K. Pessler, K.-W. Sühs, N. Novoselova, F. Klawonn, M. Kuhn, V. Kaever, K. Müller-Vahl, C. Trebst, T. Skripuletz, M. Stangel, F. Pessler: Phosphatidylcholine PC ae C44:6 in cerebrospinal fluid is a sensitive biomarker for bacterial meningitis. Journal of Translational Medicine 18:9, (2020)
  • M.A.L. Böning, S. Trittel, P. Riese, M. van Ham, M. Heyner, M. Voss, G.P. Parzmair, F. Klawonn, A. Jeron, C.A. Guzman, L. Jänsch, B. Schraven, A. Reinhold, D. Bruder: ADAP promotes degranulation and migration of NK cells primed during in vivo Listeria monocytogenes infection in mice. Frontiers in Immunology} 10, Article 3144, (2020), doi: 10.3389/fimmu.2019.03144
  • G. Hoffmann, F. Klawonn, S. Holdenrieder: Referenzintervall-Überprüfung: Ein Konzept wird erwachsen. Trillium Diagnostik 18(1) (2020), 14-17
  • F. Klawonn, M. Orth, G. Hoffmann: Quantitative Laboratory Results: Normal or Lognormal Distribution? Journal of Laboratory Medicine 44(3) (2020), 143-150
  • P. Riese, S. Trittel, R.D. Pathirana, F. Klawonn, R.J. Cox, C.A. Guzman: Responsiveness to influenza vaccination correlates with NKG2C-expression on NK cells. Vaccines 8, 281 (2020), doi:10.3390/vaccines8020281
  • Frank Höppner, Maximilian Jahnke: Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code. Symp. Intelligent Data Analysis (IDA) 2020: 248-260
  • Frank Höppner: Multidimensional Decision Tree Splits to Improve Interpretability. International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES) 2020
  • Frank Höppner: Taking benefit from fellow students code without copying off – making better use of students collective works. 18. Fachtagung Bildungstechnologien (DELFI),2020
  • Martin N., Friedewald M., Schiering I., Mester B., Hallinan D., Jensen M. (2020) Die Datenschutz-Folgenabschätzung nach Art. 35 DSGVO: Ein Handbuch für die Praxis, Fraunhofer-Verlag, Stuttgart, http://publica.fraunhofer.de/documents/N-586394.html
  • Gabel, A.; Ertas, F.; Pleger, M.; Schiering, I. and Müller, S. (2020). Privacy-preserving Metrics for an mHealth App in the Context of Neuropsychological Studies.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF, https://doi.org/10.5220/0008982801660177.
  • Lorenz T., Schiering I. (2020) Privacy in Location-Based Services and Their Criticality Based on Usage Context. In: Privacy and Identity Management. Data for Better Living: AI and Privacy. Privacy and Identity 2019. IFIP Advances in Information and Communication Technology, vol 576. Springer, Cham, https://doi.org/10.1007/978-3-030-42504-3_29
  • Schiering, I., Mester, B., Friedewald, M. et al. Datenschutz-Risiken partizipativ identifizieren und analysieren. Datenschutz und Datensicherheit - DuD 44, 161–165 (2020)
  • Martin, N., Schiering, I. & Friedewald, M. Methoden der Datenschutz-Folgenabschätzung. Datenschutz und Datensicherheit - DuD 44, 154–160 (2020). https://doi.org/10.1007/s11623-020-1242-z
  • Martin, N., Mester, B., Schiering, I. et al. Datenschutz-Folgenabschätzung. Datenschutz und Datensicherheit - DuD 44, 149–153 (2020). https://doi.org/10.1007/s11623-020-1241-0
  • K. Gutenschwager, P. Arnold (2020): Simulation von Lieferantennetzwerken: Grundlagen und Anwendungen bei der ZF Friedrichshafen AG. In: G. Mayer, C. Pöge, S. Spieckermann, S. Wenzel (Hrsg.), Ablaufsimulation in der Automobilindustrie, Springer, Berlin, S. 247-259.

Veröffentlichungen 2019

  • Markus Wohlan, Yannik Schröder, Frank Höppner: Generating "Who Wants to Be a Millionaire?" Questions Sets Automatically from Wikidata. Int. Conf. on Semantic Systems SEMANTICS 2019
  • Frank Höppner: Measuring Instruction Comprehension by Mining Memory Traces for Early Formative Feedback in Java Courses. ACM Conf. Innovation and Technology in Computer Science Education ITiCSE 2019: 105-111
  • Frank Höppner, Maximilian Jahnke:Holistic Assessment of Structure Discovery Capabilities of Clustering Algorithms. Conf. Machine Learning and Knowledge Disocvery in Databases (ECML/PKDD) 2019: 223-239
  • F. Höppner: Measuring Instruction Comprehension by Mining Memory Traces for Early Formative Feedback in Java Courses. ITiCSE 2019
  • L. Grigull, S. Mehmecke, A.-K. Rother, S. Blöß, C. Klemann, U. Schumacher, U. Mücke, X. Kortum, W. Lechner, F. Klawonn: Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition? PLoS ONE 14(10) (2019), doi:10.1371/journal.pone.0222637
  • A. Ambrosch, K. Wahrburg, F. Klawonn: Bacterial load and pathogenic species on healthcare personnel attire (HCPA): implication of alcohol hand-rub (AHR) use, profession and duration of shift. Journal of Hospital Infection 101(4), (2019), 414-421
  • M. Caputo, B. Zoch-Lesniak, A. Karch, M. Vital, F. Meyer, F. Klawonn, A. Baillot, D.H. Pieper, R.T. Mikolajczyk: Bacterial community structure and effects of picornavirus infection on the anterior nares microbiome in early childhood. BMC Microbiology (2019) 19:1
  • M. Döring, H. Blees, N. Koller, S. Tischer-Zimmermann, M. Müsken, F. Henrich, J. Becker, E. Grabski, J. Wang, H. Janssen, W. Zuschratter, J. Neefjes, F. Klawonn, B. Eiz-Vesper, R. Tampe, U. Kalinke: Modulation of TAP-dependent antigen compartmentalization during human monocyte-to-DC differentiation. Blood Advances 3 (2019), 839-850
  • R. Franke, B. Hinkelmann, V. Fetz, T. Stradal, F. Sasse, F. Klawonn, M. Brönstrup: xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action Discovery. SLAS Discovery 24(3) (2019), 213-223
  • A.-L. Sieg, A-M. Das, N.M. Muschol, A. Köhn, C. Lampe, X. Kortum, S. Mehmecke, S. Blöß, W. Lechner, F. Klawonn, L. Grigull: Künstliche Intelligenz zur diagnostischen Unterstützung ausgewählter seltener lysosomaler Speichererkrankungen: Ergebnisse einer Pilotstudie (Artificial intelligence for diagnostic support in selected rare lysosomal storage disorders: Results of a pilot study). Klinische Pädiatrie 231(02) (2019), 60-66
  • Neu, C. V., Schiering, I., & Zorzo, A. (2019). Simulating and Detecting Attacks of Untrusted Clients in OPC UA Networks. In Proceedings of the Third Central European Cybersecurity Conference (p. 11). ACM. https://doi.org/10.1145/3360664.3360675
  • Heuer T., Schiering I., Gerndt R.(2019), Privacy-centered design for social robots, Interaction Studies, Volume 20, Issue 3, Nov 2019, p. 509 - 529, https://doi.org/10.1075/is.18063.heu
  • Friedewald, M., Schiering, I., & Martin, N. (2019). Datenschutz-Folgenabschätzung in der Praxis. Datenschutz und Datensicherheit-DuD, 43(8), 473-477. https://doi.org/10.1007/s11623-019-1146-y
  • Eckhardt, K., Schiering, I., Gabel, A., Ertas, F., & Müller, S. V. (2019, September). Visual Programming for Assistive Technologies in Rehabilitation and Social Inclusion of People with Intellectual Disabilities. In Proceedings of Mensch und Computer 2019 (pp. 731-735). ACM. https://doi.org/10.1145/3340764.3344899
  • Justinger, J., Heuer, T., Schiering, I., & Gerndt, R. (2019, September). Forgetfulness as a feature: Imitation of Human Weaknesses for Realizing Privacy Requirements. In Proceedings of Mensch und Computer 2019 (pp. 825-830). ACM. https://doi.org/10.1145/3340764.3344916
  • Müller S. V., Ertas F. Aust J., Gabel A., Schiering I. (2019). Kann eine mobile Anwendung helfen abzuwaschen? Zeitschrift für Neuropsychologie, 30 (2), 123-131. https://doi.org/10.1024/1016-264X/a000256
  • Schiering I. Müller S. V. (2019). Smarte Inklusion – Smarte Devices zur Förderung der Inklusion in den ersten Arbeitsmarkt. Teilhabe, 58.
  • Schiering I. (2019), Kompetenz ist die beste Verteidigung - Über IT-Sicherheit im Zeitalter der Digitalisierung, iQ-Journal 3/2019, VDI Braunschweig, p. 8-9.
  • Gabel A., Schiering I. (2019) Privacy Patterns for Pseudonymity. In: Kosta E., Pierson J., Slamanig D., Fischer-Hübner S., Krenn S. (eds) Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data. Privacy and Identity 2018. IFIP Advances in Information and Communication Technology, vol 547. Springer, Cham, https://doi.org/10.1007/978-3-030-16744-8_11
  • Heuer T., Schiering I., Gerndt R. (2019) Me and My Robot - Sharing Information with a New Friend. In: Kosta E., Pierson J., Slamanig D., Fischer-Hübner S., Krenn S. (eds) Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data. Privacy and Identity 2018. IFIP Advances in Information and Communication Technology, vol 547. Springer, Cham, https://doi.org/10.1007/978-3-030-16744-8_13
  • K. Gutenschwager, R.D. McLeod, M.R. Friesen (2019): From OpenStreetMap and Cell Phone Data to Road Network Simulation Models. In: 2019 Winter Simulation Conference (WSC), S. 1953-1964.
  • K. Gutenschwager, J. Steinke, M. Theile, B. Wilhelm, T. Fechteler (2019): Unternehmensübergreifende Erstellung von Supply-Chain-Modellen in der Cloud. In: M. Putz, A. Schlegel (Hrsg.), Simulation in Produktion und Logistik 2019, Wissenschaftliche Scripten, Auerbach, S. 275-284.
  • K. Gutenschwager, S. Völker (2019): Entwicklung eines Bausteinkastens zur Simulation mobiler Lagerroboter. In: M. Putz, A. Schlegel (Hrsg.), Simulation in Produktion und Logistik 2019, Wissenschaftliche Scripten, Auerbach, S. 265-274.
  • K. Gutenschwager, K. Leduc-McNiven, M. Aljumaili, R.D. McLeod, M.R. Friesen (2019): Visualizing "Cognitive Fingerprints" from Simple Mobile Game Play. In: The 42nd Conference of The Canadian Medical and Biological Engineering Society, S. 1-3.
  • K. Leduc-McNiven, R.T. Dion, M. Aljumaili, R.D. McLeod, M.R. Friesen, K. Gutenschwager (2019): Serious Games and Machine Learning for Detecting Mild Cognitive Impairment. In: Proceedings on the International Conference on Artificial Intelligence (ICAI'19), S. 3-6.

Veröffentlichungen 2018

  • Katzensteiner, M., Zubke, M., Blume, C., Immenschuh, S., Gerbel, S., Marschollek, M., Kaufeld, J., Haller, H., Ludwig, W., Bott, O.J.: Screen Reject – Klinisches Data Warehouse zur Abstoßung nach Nierentransplantation – Erste Schritte. Proceedings der GMDS-Jahrestagung 2018. Osnabrück: 2018
  • Katzensteiner, M., Zubke, M., Ludwig, W., Bott, O.J.: Literaturstudie zu Expertensystemen im Kontext von Abstoßungsreaktion nach Nierentransplantation. Proceedings der GMDS-Jahrestagung 2018. Osnabrück: 2018.
  • Haverkamp, G., Bott, O.J., Ludwig, W.: Zielgruppen Usability und Akzeptanz von mobilen Applikationen in der Pflegedokumentation. Proceedings der GMDS-Jahrestagung 2018. Osnabrück: 2018
  • K. Gutenschwager, S. Völker, B. Wilhelm: (2018): Speeding up Simulation-based Optimization of Supply Networks by means of a Multi-Population Genetic Algorithm and Reuse of Partial Solutions. In: Rabe, A., Juan, A., Mustafee, A., Skoogh, A., Jain, S., Johansson, B.: Proceedings of the 2018 Winter Simulation Conference 2018: 3036-3047
  • K. Gutenschwager, M. Rabe, M. Theile, B. Wilhelm: Comparison of Approaches to Encrypt Data for Supply Chain Simulation. In: Rabe, A., Juan, A., Mustafee, A., Skoogh, A., Jain, S., Johansson, B.: Proceedings of the 2018 Winter Simulation Conference 2018, S. 3084-3095
  • F. Höppner, J.-H. Hemmje: Zur automatischen Erkennung von Fehlkonzepten bei Java-Einsteigern durch Analyse von Speicher-Protokollen. DeLFI 2018: 165-170
  • I. Bernal, J.D. Hofmann, B. Bulitta, F. Klawonn, A.-M. Michel, D. Jahn, M. Neumann-Schaal, D. Bruder, L. Jänsch: Clostridioides difficile activates human mucosal-associated invariant T cells. Frontiers in Microbiology 9, Article 2532, (2018), doi: 10.3389/fmicb.2018.02532
  • A. Bietenbeck, W.J. Geilenkeuser, F. Klawonn, M. Spannagl, M. Nauck, A. Petersmann, M.A. Thaler, C. Winter, P.B. Luppa: External quality assessment schemes for glucose measurements in Germany: factors for successful participation, analytical performance and medical impact. Clinical Chemistry and Laboratory Medicine 56(8) (2018), 1238-1250
  • A. Brennecke, S. Dueber, B. Roy, I. Gruenke, A.I. Garbe, F. Klawonn, O. Pabst, K. Kretschmer, S. Weiss: Induced B cell development in adult mice. Frontiers in Immunology 9, Article 2483 (2018), doi: 10.3389/fimmu.2018.02483
  • B. Bulitta, W. Zuschratter, I. Bernal, D. Bruder, F. Klawonn, M. von Bergen, H.S.P. Garritsen, L. Jänsch: Proteomic definition of human mucosal-associated invariant T cells determines their unique molecular effector phenotype. European Journal of Immunology 48(8) (2018), 1336-1349
  • G. Hoffmann, A. Bietenbeck, R. Lichtinghagen, F. Klawonn: Using machine learning techniques to generate laboratory diagnostic pathways -- a case study. Journal of Laboratory and Precision Medicine 3:58 (2018)
  • G. Hoffmann, F. Klawonn: Indirekte Überprüfung von Referenzintervallen: Leberwerte in neuem Licht. Trillium Diagnostik 16(2) (2018), 122-124
  • D. Lang, B.H. Schott, M. van Ham, L. Morton, L. Kulikovskaja, R. Herrera-Molina, R. Pielot, F. Klawonn, D. Montag, L. Jänsch, E.D. Gundelfinger, K.H. Smalla, I.R. Dunay: Chronic Toxoplasma infection is associated with distinct alterations in the synaptic protein composition. Journal of Neuroinflammation 15:216 (2018), doi: 10.1186/s12974-018-1242-1
  • B. Rüger, D. Pierl, M. Guber, J. Yin, M. Baur, H. Eberhard, F. Klawonn, K. Michels: Online Leakage Attribution to Exclusion Areas Prototype Application. Energy Procedia 149 (2018), 575-584
  • J. Voigt, D.F.G. Malone, J. Dias, E. Leeansyah, N.K. Björkström, H.-G. Ljunggren, L. Gröbe, F. Klawonn, M. Heyner, J.K. Sandberg, L. Jänsch: Proteome analysis of CD56neg NK cells reveals a homogeneous phenotype surprisingly similar to CD56dim NK cells. European Journal of Immunology 48(9) (2018), 1456-1469
  • X. Kortum, L. Grigull, U. Muecke, W. Lechner, F. Klawonn: Improving the Decision Support in Diagnostic Systems Using Classifier Probability Calibration. In: H. Yin, D. Camacho, P. Novais, A.J. Tallon-Ballesteros (eds.): Intelligent Data Engineering and Automated Learning -- IDEAL 2018. Springer, Berlin (2018), 419-428
  • F. Noering, K. Jonas, F. Klawonn: Assessment and Adaption of Pattern Discovery Approaches for Time Series Under the Requirement of Time Warping. In: H. Yin, D. Camacho, P. Novais, A.J. Tallon-Ballesteros (eds.): Intelligent Data Engineering and Automated Learning -- IDEAL 2018. Springer, Berlin (2018), 285-296
  • A. Cristal, O.S. Unsal, X. Martorell, P. Carpenter, R. De La Cruz, L. Bautista, D. Jimenez, C. Alvarez, B. Salami, S. Madonar, M. Pericas, P. Trancoso, M. vor dem Berge, G. Billung-Meyer, S. Krupop, W. Christmann, F. Klawonn, A. Mihklafi, T. Becker, G. Gaydadjiev, H. Salomonsson, D. Dubhashi, O. Port, Y. Etsion, V. Nowack, C. Fetzer, J. Hagemeyer, T. Jungeblut, N. Kucza, M. Kaiser, M. Porrmann, M. Pasin, V. Schiavoni, I. Rocha, C. Göttel, P. Felber: LEGaTO: towards energy-efficient, secure, fault-tolerant toolset for heterogeneous computing. Proc. of the 15th ACM International Conference on Computing Frontiers, ACM, New York (2018), 276-278
  • Heuer T., Schiering I., Gerndt R., Privacy and Socially Assistive Robots - A Meta Study. In: The Smart Revolution. Privacy and Identity 2017. IFIP Advances in Information and Communication Technology, vol 526. Springer, Cham., 2018
  • Alexander Gabel, Tanja Heuer, Ina Schiering and Reinhard Gerndt. Jetson, where is the Ball? Using Neural Networks for Ball Detection at RoboCup 2017, RoboCup Symposium 2018, Springer
  • Gabel A., Schiering I., Müller S.V., Ertas F., mHealth Applications for Goal Management Training - Privacy Engineering in Neuropsychological Studies. In: The Smart Revolution. Privacy and Identity 2017. IFIP Advances in Information and Communication Technology, vol 526. Springer, Cham., 2018.
  • Ertas, F., Knop, A., Gabel, A., Schiering, I., Müller, S.V. (2018). Digitale Realisierung des Goal Management Trainings für Patient/innen mit exekutiver Dysfunktion dargestellt anhand zweier Einzelfälle. Zeitschrift für Neuropsychologie, 29, 3, 200-201.
  • Schiering, I., Gabel, A., Ertas, F., Müller, S.V. (2018), Smart Devices und Augmented Reality in Therapie und Rehabilitation - Innovationspotential und Risiken zu Datenschutz und IT Sicherheit. Zeitschrift für Neuropsychologie, 29, 3, 182.
  • Tanja Heuer, Ina Schiering, Reinhard Gerndt, Transparency for Social Robots, The International PhD Conference on Safe and Social Robotics (SSR-2018), (Proceedings)
  • T. Fechteler, K. Gutenschwager (2018): Kollaborative Materialflusssimulation: Firmenübergreifend die Lieferkette optimieren.IT&Production 6, S. 58-60.
  • A. Ambrosch, A. Klinger, D. Luber, C. Arp, M. Lepiorz, S. Schroll, F. Klawonn: Symptomatologie und klinischer Verlauf bei hospitalisierten Erwachsenen mit Virusinfektionen durch Influenza A und Respiratory Syncytial-Virus (RSV). Deutsche Medizinische Wochenschrift 143(09) (2018), 68-75
  • P. Croce, F. Marsili, F. Klawonn, P. Formichi, F. Landi: Evaluation of statistical parameters of concrete strength from secondary experimental test data. Construction & Building Materials 163 (2018), 343-359
  • T. Klingen, S. Reimering, J. Loers, K. Mooren, F. Klawonn, T. Krey, G. Gabriel, A. McHardy: Sweep Dynamics (SD) plots: Computational identification of selective sweeps to monitor the adaptation of influenza A viruses. Scientific Reports 8, Article number: 373 (2018), doi:10.1038/s41598-017-18791-z
  • M. Kuhn, K.-W. Sühs, M.K. Akmatov, F. Klawonn, J. Wang, T. Skripuletz, V. Kaever, M. Stangel, F. Pessler: Mass-spectrometric profiling of cerebrospinal fluid reveals metabolite biomarkers for CNS involvement in varicella zoster virus reactivation. Journal of Neuroinflammation 15:20 (2018), doi: 10.1186/s12974-017-1041-0
  • D. Peralta, C. Bergmeir, M. Krone, M. Galende, M. Menendez, G.I. Sainz-Palmero, C. Martinez Bertrand, F. Klawonn, J.M. Benitez: Multiobjective Optimization for Railway Maintenance Plans. Journal of Computing in Civil Engineering 32(3), 2018, doi: 10.1061/(ASCE)CP.1943-5487.0000757
  • F. Klawonn: Exploring Time-Resolved Data for Patterns and Validating Single Clusters. In: S. Mostaghim, A. Nürnberger, C. Borgelt (eds.): Frontiers in Computational Intelligence. Springer, Cham (2018), 61-71

Veröffentlichungen 2017

  • K. Gutenschwager, M. Rabe, S. Spieckermann, S. Wenzel (2017): Simulation in Produktion und Logistik, Berlin, Springer Vieweg
  • S. Völker, K. Gutenschwager, P-M. Schmidt (2017): Einsatz von Varianzreduktionstechniken in aktuellen Simulationswerkzeugen. In S. Wenzel, T. Peter (Hrsg.): Simulation in Produktion und Logistik 2017, 17. ASIM Fachtagung Kassel, 20.-22. September 2017, Kassel University Press, S. 179 -188
  • A. Bakuli, F. Klawonn, A. Karch, R. Mikolajczyk: Effects of pathogen dependency in a multi-pathogen infectious disease system including population level heterogeneity - a simulation study. Theoretical Biology and Medical Modelling 14:26 (2017), doi: 10.1186/s12976-017-0072-7
  • A. Bietenbeck, M.A. Thaler, P.B. Luppa, F. Klawonn: Stronger Together: Aggregated Z-values of Traditional Quality Control Measurements and Patient Medians Improve Detection of Biases. Clinical Chemistry 63(8) (2017), 1377-1387
  • S. Blöß, C. Klemann, A.-K. Rother, S. Mehmecke, U. Schumacher, U. Mücke, M. Mücke, C. Stieber, F. Klawonn, X. Kortum, W. Lechner, L. Grigull: Diagnostic needs for rare diseases and shared prediagnostic phenomena: Results of a German-wide expert Delphi survey. PLoS ONE 12(2) (2017), doi:10.1371/journal.pone.0172532
  • M. van Ham, R. Teich, L. Philipsen, J. Niemz, N. Amsberg, J. Wissing, M. Nimtz, L. Gröbe, S. Kliche, N. Thiel, F. Klawonn, M. Hubo, H. Jonuleit, P. Reichardt, A. Müller, J. Huehn, L. Jänsch: TCR signalling network organization at the immunological synapses of regulatory T cells. European Journal of Immunology 47(12) (2017), 2043-2058
  • G. Hoffmann, F. Klawonn, R. Lichtinghagen, M. Orth: Der zlog-Wert als Basis für die Standardisierung von Laborwerten. LaboratoriumsMedizin 41(1) (2017), 23-32
  • G. Hoffmann, F. Klawonn: Farbgestaltung von Befundberichten: Von der Bioinformatik lernen. Trillium Diagnostik 15(3) (2017), 161-163
  • H. Lingel, J. Wissing, A. Arra, D. Schanze, S. Lienenklaus, F. Klawonn, M. Pierau, M. Zenker, L. Jänsch, M.C. Brunner-Weinzierl: CTLA-4-mediated posttranslational modifications direct cytotoxic T-lymphocyte differentiation. Cell Death and Differentiation 24 (2017), 1739-1749
  • U. Mücke, C. Klemann, U. Baumann, A. Meyer-Bahlburg, X. Kortum, F. Klawonn, W. Lechner, L. Grigull: Patient's experience in pediatric PID: Computerized classification of questionnaires. Frontiers in Immunology 8 (2017), doi:10.3389/fimmu.2017.00384
  • C. Stieber, M. Mücke, I.C. Windheuser, L. Grigull, F. Klawonn, S. Tunc, A. Münchau, T. Klockgether: Kurze Wege zur Diagnose - Eine Handlungsanweisung für Patienten ohne Diagnose. Bundesgesundheitsblatt 60 (5), (2017), 517-522
  • K. Tschumitschew, F. Klawonn: Effects of drift and noise on the optimal sliding window size for data stream regression models. Communications in Statistics - Theory and Methods 46(10) (2017), 5109-5132
  • X. Kortum, L. Grigull, W. Lechner, F. Klawonn: A Dynamic Adaptive Questionnaire for Improved Disease Diagnostics. In: N. Adams, A. Tucker, D. Weston (eds.): Advances in Intelligent Data Analysis XVI, Springer, Cham (2017), 162-172
  • T. Heuer, I. Schiering and R. Gerndt. "MuseumsBot-An Interdisciplinary Scenario in Robotics Education." International Conference on Robotics and Education RiE 2017. Springer, Cham, 2017, pp. 141-153.
  • I. Schiering, A. Hitzmann, O. Krebs, T. Lorenz, Security-Monitoring beim Pairing in Wireless Sensor Networks, DACH Security (2017): pp. 114-125.
  • M. Böhm, I. Schiering, D. Wermser, Security of IoT Cloud Services - A User-Oriented Test Approach, Central European Cybersecurity Conference CECC 2017, Maribor Press, Advances in Cybersecurity, 2017, pp. 115-129.
  • F. Höppner: Improving time series similarity measures by integrating preprocessing steps. Data Min. Knowl. Discov. 31(3): 851-878 (2017)
  • R. Goltermann, F. Höppner: Internalizing a Viable Mental Model of Program Execution in First Year Programming Courses. Proc. 3rd Workshop "Automatische Bewertung von Programmieraufgaben" (ABP 2017), Potsdam, 2017
  • F. Höppner, T. Sobek: A Multiscale Bezier-Representation for Time Series that Supports Elastic Matching. Proc. European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases, Skopje, 2017
  • M. Omar, F. Klawonn, S. Brand, M. Stiesch, C. Krettek, J. Eberhard: Transcriptome-Wide High-Density Microarray Analysis Reveals Differential Gene Transcription in Periprosthetic Tissue From Hips With Chronic Periprosthetic Joint Infection vs Aseptic Loosening. The Journal of Arthroplasty 32 (2017), 234-240

Veröffentlichungen 2016

  • V.K. Nguyen, F. Klawonn, R. Mikolajczyk, E.A. Hernandez-Vargas: Analysis of practical identifiability of a viral infection model. PLoS ONE 11(12) (2016)
  • F. Marsili, P. Croce, F. Klawonn, F. Landi: A Bayesian Network for the Definition of Probability Models for Compressive Strength of Concrete Homogeneous Population. In: R. Caspeele, L. Taerwe, D. Proske (eds.): 14th International Probabilistic Workshop, Springer, Cham (2016), 269-283
  • F. Marsili, P. Croce, F. Klawonn, A. Vignoli, S. Boschi, F. Landi: A Bayesian Network for the Definition of Probability Models for Masonry Mechanical Parameters. In: R. Caspeele, L. Taerwe, D. Proske (eds.): 14th International Probabilistic Workshop, Springer, Cham (2016), 253-268
  • T. Sobek, F. Höppner: Visual Perception of Discriminative Landmarks in Classified Time Series. In: H. Boström, A.J. Knobbe, C. Soares, P. Papapetrou (eds): 15th International Symposium on Advances in Intelligent Data Analysis - IDA 2016. Springer (2016), 73-85
  • H. Yin, Y. Gao, B. Li, D. Zhang, M. Yang, Y. Li, F. Klawonn, A.J. Tallon-Ballesteros (eds.): Intelligent Data Engineering and Automated Learning - IDEAL 2016. Springer, Cham (2016)
  • G. Hoffmann, F. Klawonn: Big Data in der Labormedizin: Von der Utopie zur Strategie. Trillium Diagnostik 14(1) (2016), 24-26
  • A. Khaledi, M. Schniederjans, S. Pohl, R. Rainer, U. Bodenhofer, B. Xia, F. Klawonn, S. Bruchmann, M. Preusse, D. Eckweiler, A. Dötsch, S. Häussler: Transcriptome profiling of antimicrobial resistance in Pseudomonas aeruginosa. Antimicrobial Agents and Chemotherapy 60(8) (2016), 4722-4733
  • A. Kummer, G. Nishanth, J. Koschel, F. Klawonn, D. Schlüter, L. Jänsch: Listeriosis downregulates hepatic cytochrome P450 enzymes in sublethal murine infection. Proteomics - Clinical Applications 10(9-10) (2016), 1025-1035
  • F. Klawonn: Exploring data sets for clusters and validating single clusters. Procedia Computer Science 96 (2016), 1381-1390
  • F. Klawonn, J. Wang, I. Koch, J. Eberhard, M. Omar: HAUCA Curves for the Evaluation of Biomarker Pilot Studies with Small Sample Sizes and Large Numbers of Features. In: H. Boström, A. Knobbe, C. Soares, P. Papapetrou (eds.): Advances in Intelligent Data Analysis XV. Springer, Cham (2016), 356-367
  • X. Kortum, L. Grigull, U. Muecke, W. Lechner, F. Klawonn: Diagnosis Support for Orphan Diseases: A Case Study Using a Classifier Fusion Method. In: H. Yin, Y. Gao, B. Li, D. Zhang, M. Yang, Y. Li, F. Klawonn, A.J. Tallon-Ballesteros (eds.): Intelligent Data Engineering and Automated Learning - IDEAL 2016. Springer, Cham (2016), 379-385
  • Schiering, I., Heuer, T., Seeger, M., Klawonn F., Gabel, A. (2016). Recognizing Time-Efficiently Local Botnet Infections – A Case Study. In Proc. 11th Int. Conf. on Availability, Reliability and Security (ARES), Salzburg, Austria, 2016, pp. 304-311
  • Ohms, J., Schiering, I., Wentscher, P., Kaltefeiter, R. (2016). Reputation und Threat Information als Ergänzung zu Black Lists. In Proc. DACH Security, pages 87–97.
  • Casilag, F., Lorenz, A., Krüger, J., Klawonn, F., Weiß, S., and Häussler, S. (2016). The LasB elastase of Pseudomonas aeruginosa acts in concert with alkaline protease AprA to prevent flagellin-mediated immune recognition. Infection and Immunity, 84, 162–171.
  • Grigull, L., Lechner, W., Petri, S., Kollewe, K., Dengler, R., Mehmecke, S., Schumacher, U., Lücke, T., Schneider-Gold, C., Köhler, C., Güttsches, A.-K., Kortum, X., Klawonn, F. (2016). Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial. BMC Medical Informatics and Decision Making,16:31.
  • Seeger, B., Klawonn, F., Nguema Bekale, B., Steinberg, P. (2016). Mixture effects of estrogenic pesticides at the human estrogen receptor α and β. PLoS ONE, 11(1).
  • Seeger, B., Klawonn, F., Nguema Bekale, B., Steinberg, P. (2016). The ability of the YAS and AR CALUX assays to detect the additive effects of anti-androgenic fungicide mixtures. Toxicology Letters 241, 193–199.

Veröffentlichungen 2015

  • Vogel, S., Grabski, E., Buschjäger, D., Klawonn, F., Döring, M., Wang, J., Fletcher, E., Bechmann, I., Witte, T., Durisin, M., Schraven, B., Mangsbo, S.M., Schönfeld, K., Czeloth, N., Kalinke, U. (2016). Antibody induced CD4 down-modulation of T cells is site-specifically mediated by CD64+ cells. Scientific Reports, 5:18308
  • Höppner, F. (2015). Optimal filtering for time series classification. In Proc. 16th Int. Conf. Intelligent Data Engineering and Automated Learning, pages 26–35.
  • König, J., Grigull, L., Fritsch, H.-W., and Klawonn, F. (2015). IT-Unterstützung zur Diagnosefindung seltener Erkrankungen – Umfassende Datenanalyse bei komplexen Fällen. Der Klinikarzt, 44, 16–21.
  • Klawonn, F., Kruse, R., and Winkler, R. (2015). Fuzzy clustering: More than just fuzzification. Fuzzy Sets and Systems, 281, 272–279.
  • Novoselova, N., Wang, J., and Klawonn, F. (2015). Optimized leaf ordering with class labels for hierarchical clustering. Journal of Bioinformatics and Computational Biology, 13(4).
  • Pfaender, S., Walter, S., Todt, D., Behrendt, P., Doerrbecker, J., Woelk, B., Engelmann, M., Gravemann, U., Seltsam, A., Steinmann, J., Burbelo, P., Klawonn, F., Feige, K., Pietschmann, Z., and Cavalleri, J.M.V. Steinman, E. (2015). Assessment of cross-species transmission of hepatitis C-related non-primate hepacivirus in a population of humans at high risk of exposure. Journal of General Virology, 96, 2636–2642.
  • Preusse, M., Schughart, K., Wilk, E., Klawonn, F., and Pessler, F. (2015). Hematological parameters in the early phase of influenza A virus infection in differentially susceptible inbred mouse strains. BMC Research Notes, 8:225.
  • Rand, U., Riedel, J. Hillebrand, U., Shin, D., Willenberg, S., Behme, S., Klawonn, F., Köster, M., Hauser, H., and Wirth, D. (2015). Single-cell analysis reveals heterogeneity in onset of transgene expression from synthetic tetracycline-dependent promoters. Biotechnology Journal, 10, 323–331.
  • Rother, A.-K., Schwerk, N., Brinkmann, F., Klawonn, F., Lechner, W., and Grigull, L. (2015). Diagnostic support for selected paediatric pulmonary diseases using answer-pattern recognition in questionnaires based on combined data mining applications – a monocentric observational pilot study. PLoS ONE, 10 (8).
  • Szafranski, S., Wos-Oxley, M., Vilchez-Vargas, R., J’auregui, R., Plumeier, I., Klawonn, F., Tomasch, J., Meisinger, C., Kühnisch, J., Sztajer, H., Pieper, D., and Wagner-Döbler, I. (2015). High-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis. Applied and Environmental Microbiology, 81, 1047–1058.
  • Thumfart, J., Abidi, N., Mindt, S., Costina, V., Hofheinz, R., Klawonn, F., Neumaier, M., and Findeisen, P. (2015). LC/MS based monitoring of endogenous decay markers for quality assessment of serum specimens. Proteomics & Bioinformatics, 8:5, 91–97.
  • Winkler, R., Klawonn, F., and Kruse, R. (2015). Prototype based fuzzy clustering algorithms in high-dimensional feature space. In L. Magdalena, J. Verdegay, and F. Esteva, editors, Enric Trillas: A Passion for Fuzzy Sets: A Collection of Recent Works on Fuzzy Logic, pages 233–243. Springer, Cham.
  • Wirsing, L., Klawonn, F., Sassen, W., Lünsdorf, H., Probst, C., Hust, M., Mendel, R., Kruse, T., and Jänsch, L. (2015). Linear discriminant analysis identifies mitochondrially localized proteins in neurospora crassa. Journal of Proteome Research, 14, 3900–3911.
  • Worthmann, H., Tryc, A., Dirks, M., Schuppner, R., Brand, K., Klawonn, F., Lichtinghagen, R., and Weissenbor, K. (2015). Lipopolysaccharide binding protein, interleukin-10, interleukin-6 and C-reactive protein blood levels in acute ischemic stroke patients with post-stroke infection. Journal of Neuroinflammation, 12:13.
  • Wu, C.-F., Andzinski, L., Kasnitz, N., Kröger, A., Klawonn, F., Lienenklaus, S., Weiss, S., and Jablonska, J. (2015). The lack of type I interferon induces neutrophil-mediated pre-metastatic niche formation in the mouse lung. International Journal of Cancer, 137(4), 837–847.
  • Zhang, X., Klawonn, F., Grigull, L., and Lechner, W. (2015). VoQs: A web application for visualization of questionnaire surveys. In T. Fromont, E. De Bie and M. van Leeuwen, editors, A Passion for Fuzzy Sets: A Collection of Recent Works on Fuzzy Logic, pages 334–343, Cham. Springer.
  • Klawonn, F., Höppner, F., Jayaram, B. (2015). What are clusters in high dimensions and are they difficult to find? In F. Masulli, A. Petrosino, S. Rovetta, editors, Clustering High-Dimensional Data, pages 14–33. Springer, Berlin.
  • Fiedler, G., Schiering, I. (2015). Informatik und Archivkunde: Zwei, die sich nicht treffen? Archiv-Nachrichten Niedersachsen, 19, 34–39.

Veröffentlichungen 2014

  • Haux, R., Ludwig, W., et al.: Information and communication technologies for promoting and sustaining quality of life, health and self-sufficiency in ageing societies. Outcomes of the lower saxony research network Design of Environments for Ageing (GAL). Inform Health Soc Care. 2014 Sep-Dec;39(3-4):166-87.
  • Fechtler, T. and Gutenschwager, K. (2014). Die Landkarte zeigt, wie gut es funktioniert. IT&Production, 9, 64–65.
  • Gerndt, R., Schiering, I., and Lüssem, J. (2014). Elements of scrum in a students robotics project: a case study. Journal of Automation Mobile Robotics and Intelligent Systems, 8.
  • Höppner, F. (2014). A subspace filter supporting the discovery of small clusters in very noisy datasets. Proc. 26th Int. Conf. on Scientific and Statistical Database Management - SSDBM ’14. Best Paper Award
  • Höppner, F. (2014). Less is more: Similarity of time series under linear transformations. In Proc. SIAM Int. Conf. Data Mining (SDM), pages 560–5 68.
  • Höppner, F. and Peter, S. (2014). Temporal interval pattern languages to characterize time flow. Wiley Interdisc. Rev.: Data Mining and Knowledge Discovery, 4(3), 196–212.
  • Kahrmann, J. and Schiering, I. (2014). Patterns in privacy - A pattern-based approach for assessments. In Privacy and Identity Management for the Future Internet in the Age of Globalisation - 9th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2 International Summer School, Patras, Greece, September 7-12, 2014, Revised Selected Papers, pages 153–166.
  • Klawonn, F. and Hoffmann, G. (2014). Die Suche nach dem perfekten Algorithmus: Auswertung molekularbiologischer Massendaten. Trillium Diagnostik, 12, 64–65.
  • Milanez-Almeida, P., Klawonn, F., Meyer-Hermann, M., and Huehn, J. (2014). Differential control of immune cell homeostasis by Foxp3+ regulatory T cells in murine peripheral lymph nodes and spleen. European Journal of Microbiology and Immunology, 4, 147–155.
  • Montvida, O. and Klawonn, F. (2014). Relative cost curves: An alternative to AUC and an extension to 3-class problems. Kybernetika, 50, 647–6 60.
  • Novoselova, N., Della Beffa, C., Wang, J., Li, J., Pessler, F., and Klawonn, F. (2014). HUM calculator and HUM package for R: easy-to-use software tools for multicategory receiver operating characteristic analysis. Bioinformatics, 30, 1635–1 636.
  • Rodeck, M., Voigt, C., Schnütgen, A., Schiering, I., and Decker, R. (2014). Toolgestützte Assessments zu Datenschutz und Datensicherheit in kleinen und mittelständischen Unternehmen. In 44. Jahrestagung der Gesellschaft für Informatik, Informatik 2014, Big Data - Komplexität meistern, 22.-26. September 2014 in Stuttgart, Deutschland, pages 575–586.
  • Schiering, I., Hitzmann, A., and Gerndt, R. (2014). Testing in robotics student teams-a case study about failure and motivation. In Proceedings of the 5th International Conference on Robotics in Education and Teaching Robotics & Teaching with Robotics, Padova, Italy.
  • Schweier, A. and Höppner, F. (2014). Finding the intrinsic patterns in a collection of time series. In Proc. 14th Int. Symp. on Intelligent Data Analysis, pages 286–297. Springer.
  • Shevchuk, O., Abidi, N., Klawonn, F., Wissing, J., Nimtz, M., Kugler, C., Steinert, M., Goldmann, T., and Jänsch, L. (2014). HOPE-fixation of lung tissue allows retrospective proteome and phosphoproteome studies. Journal of Proteome Research, 13, 5230–5239.

Veröffentlichungen 2013

  • Abidi, N., Klawonn, F., and Thumfart, J. (2013). Time point estimation of a single sample from high throughput experiments based on time-resolved data and robust correlation measures. In A. Tucker, F. Höppner, A. Siebes, and S. Swift, editors, Advances in Intelligent Data Analysis XII, pages 32–43, Berlin. Springer.
  • Bodenhofer, U., Krone, M., and Klawonn, F. (2013). Testing noisy numerical data for monotonic association. Information Sciences, 243, 21–3 7.
  • Della Beffa, C., Slansky, E., Pommerenke, C., Klawonn, F., Li, J., Dai, L., Schumacher Jr., H., and Pessler, F. (2013). The relative composition of the inflammatory infiltrate as an additional tool for synovial tissue classification. PLoS ONE, 8.
  • Höppner, F., Peter, S., and Berthold, M. R. (2013). Enriching multivariate temporal patterns with context information to support classification. In Computational Intelligence in Intelligent Data Analysis, volume 445 of
    Studies in Computational Intelligence, pages 195–206. Springer.
  • Huang, G., Liu, X., He, J., Klawonn, F., and Yao, G., editors (2013). Health Information Science (HIS 2013). Springer, Berlin.
  • Ince, K., Schneider, T., and Klawonn, F. (2013). Analysis of sequential data in tool manufacturing of Volkswagen AG. In L. Nakamatsu, K. Jain, editor, The Handbook on Reasoning-Based Intelligent Systems, pages 555–
    574. World Scientific, Singapore.
  • Ince, K. and Klawonn, F. (2013a). Decision and regression trees in the context of attributes with different granularity levels. In C. Borgelt, M. Gil, J. Sousa, and M. Verleysen, editors, Towards Advanced Data
    Analysis by Combining Soft Computing and Statistics, pages 331–342. Springer, Berlin.
  • Ince, K. and Klawonn, F. (2013b). Handling different levels of granularity within naive Bayes classifiers. In H. Yin, K. Tang, Y. Gao, F. Klawonn, M. Lee, T. Li, B. Weise, and X. Yao, editors, Intelligent Data Engineering
    and Automated Learning – IDEAL 2013, pages 521–528, Berlin. Springer.
  • Klawonn, F. (2013). What can fuzzy cluster analysis contribute to clustering of high-dimensional data? In F. Masulli, G. Pasi, and R. Yager, editors, Fuzzy Logic and Applications (WILF 2013), pages 1–14, Cham. Springer.
  • Klawonn, F., Jayaram, B., Crull, K., Kukita, A., and Pessler, F. (2013a). Analysis of contingency tables based on generalised median polish with power transformations and non-additive models. Health Information Science and Systems, 1:11.
  • Klawonn, F., Lechner, W., and Grigull, L. (2013b). Case-centred multidimensional scaling for classification visualisation in medical diagnosis. In G. Huang, X. Liu, J. He, F. Klawonn, and G. Yao, editors, Health Information Science (HIS 2013), pages 137–148, Berlin. Springer.
  • Köhne, H. and Gutenschwager, K. (2013). Einlastung von Montageaufträgen: Von der Simulation zur Integration in das operative Planungssystem der Fa. Nobilia. In W. Dangelmaier, C. Laroque, and A. Klaas, editors, Simulation in Produktion und Logistik: Entscheidungsunterstützung von der Planung bis zur Steuerung, pages 247–257, Paderborn. HNI-Verlagsschriftenreihe.
  • Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., and Held, P. (2013). Computational Intelligence: A Methodological Introduction. Springer, London.
  • Peter, S. and Höppner, F. (2013). Pattern graphs: Combining multi-variate time series and labelled interval sequences for classification. In Proc. SGAI Int. Conf. on Artificial Intelligence, pages 5–18.
  • Preusse, M., Tantawy, M., Klawonn, F., Schughart, K., and Pessler, F. (2013). Infection- and procedure-dependent effects on pulmonary gene expression in the early phase of influenza A virus infection in mice. BMC Microbiology, 13:293.
  • Rabe, M., Gutenschwager, K., Fechteler, T., and Sari, M. (2013). A data model for carbon footprint simulation in consumer goods supply chains. In Winter Simulation Conference 2013, Washington, DC USA, December 8-11, 2013, pages 2677–2 688.
  • Rother, S. V. and Schiering, I. (2013). Privacy in the life-cycle of IT services - an investigation of process reference models. In Privacy and Identity Management for Emerging Services and Technologies - 8th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6 International Summer School, Nijmegen, The Netherlands, June 17-21, 2013, Revised Selected Papers, pages 102–113.
  • Scheiter, M., Bulitta, B., van Ham, M., Klawonn, F., König, S., and Jänsch, L. (2013a). Protein kinase inhibitors CK59 and CID755673 alter primary human NK cell effector functions. Frontiers in Immunology, 4:66.
  • Scheiter, M., Lau, U., van Ham, M., Bulitta, B., Gröbe, L., Garritsen, H., Klawonn, F., König, S., and Jänsch, L. (2013b). Proteome analysis of distinct developmental stages of human natural killer cells. Molecular & Cellular Proteomics, 12, 1099–1114.
  • Tschumitschew, K. and Klawonn, F. (2013a). Change detection based on the distribution of p-values. In C. Borgelt, M. Gil, J. Sousa, and M. Verleysen, editors, Towards Advanced Data Analysis by Combining Soft Computing and Statistics, pages 191–203. Springer, Berlin.
  • Tschumitschew, K. and Klawonn, F. (2013b). High against low quantile comparison for biomarker and classifier evaluation. In H. Yin, K. Tang, Y. Gao, F. Klawonn, M. Lee, T. Li, B. Weise, and X. Yao, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2013, pages 561–568, Berlin. Springer.
  • Tucker, A., Höppner, F., Siebes, A., and Swift, S., editors (2013). Advances in Intelligent Data Analysis XII, Lecture Notes in Computer Science. Springer.
  • Winkler, R., Klawonn, F., and Kruse, R. (2013). A new distance function for prototype based clustering algorithms in high dimensional spaces. In P. Giudici, S. Ingrassia, and M. Vichi, editors, Statistical Models for Data Analysis, pages 371–378. Springer, Berlin.
  • Wuestefeld, T., Pesic, M., Rudalska, R., Dauch, D., Longerich, T., Kang, T.-W., Yevsa, T., Heinzmann, F., Hoenicke, L., Hohmeyer, A., Potapova, A., Rittelmeier, I., Jarek, M., Geffers, R., Scharfe, M., Klawonn, F., Schirmacher, P., Malek, N., Ott, M., Nordheim, A., Vogel, A., Manns, M., and Zender, L. (2013). A direct in vivo RNAi screen identifies MKK4 as a key regulator of liver regeneration. Cell, 153, 389–401.
  • Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Li, B. Weise, T., and Yao, X., editors (2013). Intelligent Data Engineering and Automated Learning – IDEAL 2013. Springer, Berlin.
  • Dochkova, J., Krone, M., and Mengersen, I. (2013). New values for cut set Catalan numbers. Utilitas Mathematica, 92, 291–293.

Veröffentlichungen 2012

  • Ludwig, W.: Ein Ansatz zur transinstitutionellen Geschäftsmodellierung altersbezogener hybrider Dienstleistungen auf Basis Assistierender Gesundheitsdienstleistungen. Konzeption und exemplarische Instanziierung am Beispiel des Projektes eHealth.Braunschweig. Dissertation. Norderstedt: Books on Demand; 2012
  • von Bargen, T., Wolf, K.-H., Lipprandt, M., Ludwig, W., Frenken, T., Haux, R., et al.: Distribution und Integration von Assessmentdaten aus der häuslichen Umgebung in patientenzentrierten Gesundheitsnetzwerken. In: Proceedings of the 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), 42. Jahrestagung der Gesellschaft für Informatik (GI). Braunschweig, 2012 Sep 16-21.
  • Hellrung, N., Ludwig, W., Frenken, T., Lipprandt, M., Stehen, E.-E., Helmer, A., et al.: Einbettung assistierender Technologien in Gesundheitsnetzwerke – von der Wohnung zum Arzt. In: Shire, K.A., Leimeister, J.M., Hrsg. Technologiegestützte Dienstleistungsinnovation in der Gesundheitswirtschaft, Wiesbaden: Springer Gabler; 2012. S 241-262.
  • Ludwig, W., von Bargen, T., Hellrung, N., Wagner, M., Haux, R.: Stakeholder Assistierender Gesundheitstechnologien - Eine Analyse der an der Leistungserstellung häuslicher, IT basierter Gesundheitsdienstleistungen beteiligter Nutzergruppen. In: Bieber, D., Kött, A., Hrsg. Mit AAL-Dienstleistungen altern. Nutzerbedarfsanalysen im Kontext des Ambient Assisted Living. Saarbrücken: iso-Institut; 2012. S. 177-96
  • Gusew, N., Ludwig, W., et al.: A Regional Health Care Network: eHealth.Braunschweig. Domain Fields and Architectural Challenges. Methods Inf Med. 2012;51(3):199-209.
  • Wagner, M., Dresing, K., Ludwig, W., Ahrens, C.A., Bott, O.J.: SIScaR-GPU: graphics processing unit accelerated simulation and visualization of intraoperative scattered radiation to support radiation protection training. Stud Health Technol Inform. 2012;180:968-72.
  • Ludwig, W., Wolf, K.H., Duwenkamp, C., Gusew, N., Hellrung, N., Marschollek, M., et al.: Health-enabling technologies for the elderly - An overview of services based on a literature review. Comput Methods Programs Biomed. 2012 May;106(2):70-8.
  • Winter, A., Alt, R., Ehmke, J., Haux, R., Ludwig, W., Mattfeld, D., et al.: Manifest Kundeninduzierte Orchestrierung komplexer Dienstleistungen. Gestaltung eines Paradigmenwechsels. Informatik Spektrum. Epub 2012 Jun 26
  • Gernert, C., Berger, E., Klawonn, F., and Jänsch, L. (2012). Tackling misleading peptide regulation fold changes in quantitative proteomics. In M. Rocha, N. Luscombe, F. Fdez-Riverola, and J. Corchado Rodríguez, editors, 6th International Conference on Practical Applications of Computational Biology & Bioinformatics, pages 269–276, Berlin. Springer.
  • Grigull, L., Betzel, C., Schumacher, U., Rother, A.-K., Mücke, U., Klawonn, F., and Lechner, W. (2012). Sollten Kinderärzte zur Unterstützung bei der Diagnostik einen Computer nutzen? Pädiatrische Praxis, 79, 545–555.
  • Gutenschwager, K., Radtke, A., Völker, S., and Zeller, G. (2012). The shortest path: comparison of different approaches and implementations for the automatic routing of vehicles. In Winter Simulation Conference, WSC ’12, Berlin, Germany, December 9-12, 2012, pages 3312–3323.
  • Hollmén, J., Klawonn, F., and Tucker, A., editors (2012). Advances in Intelligent Data Analysis XI. Springer, Berlin.
  • Jayaram, B. and Klawonn, F. (2012a). Can fuzzy clustering avoid local minima and undesired partitions? In C. Moewes and A. Nürnberger, editors, Computational Intelligence in Intelligent Data Analysis, pages 31–44. Springer, Berlin.
  • Jayaram, B. and Klawonn, F. (2012b). Can unbounded distance measures mitigate the curse of dimensionality? International Journal of Data Mining, Modelling and Management, 4, 361–383.
  • Jayaram, B. and Klawonn, F. (2012c). Generalised median polish based on additive generators. In R. Kruse, M. Berthold, C. Moewes, M. Gil, P. Grzegorzewski, and O. Hryniewicz, editors, Synergies of Soft Computing and Statistics for Intelligent Data Analysis, pages 439–448, Berlin. Springer.
  • Johl, T., Nimtz, M., Jänsch, L., and Klawonn, F. (2012). Detecting glycosylations in complex samples. In L. Iliadis, I. Maglogiannis, and H. Papadopoulos, editors, Artificial Intelligence Applications and Innovations Part 1, pages 234–243, Heidelberg. Springer.
  • Klawonn, F. (2012). Significance tests to identify regulated proteins based on a large number of small samples. Kybernetika, 48, 478–4 93.
  • Klawonn, F., Abidi, N., Berger, E., and Jänsch, L. (2012a). Curve fitting for short time series data from high throughput experiments with correction for biological variation. In J. Hollmén, F. Klawonn, and A. Tucker, editors, Advances in Intelligent Data Analysis XI, pages 150–160, Berlin. Springer.
  • Klawonn, F., Crull, K., Kukita, A., and Pessler, F. (2012b). Median polish with power transformations as an alternative for the analysis of contingency tables with patient data. In J. He, X. Liu, E. Krupinski, and G. Xu, editors, Health Information Science: First International Conference, HIS 2012, pages 25–35, Berlin. Springer.
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