Office C430
Email: ralf.eggelingspam prevention@uni-tuebingen.de
Phone: +49 7071 29 70418
 

Teaching

• Winter 2023/24
  • Seminar: Machine Learning for Health
  • Proseminar: Grundlagen der Medizininformatik
• Summer 2023
  • Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
• Winter 2022/23
  • Seminar: Machine Learning for Health
  • Proseminar: Grundlagen der Medizininformatik
• Summer 2022
  • Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
• Summer 2021
  • Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
  • Seminar: Machine Learning for Health
• Winter 2020/21
  • Seminar: Machine Learning for Health
• Summer 2020
  • Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
• Winter 2019/20
  • Seminar: Machine Learning for Health
• Summer 2019
  • Seminar: Probabilistic Graphical Models and Probabilistic Programming
• Winter 2018/19
  • Seminar: Machine Learning for Health (with Nico Pfeifer)
     

Publications

• Kanika Vanshylla, Pinkus Tober-Lau, Henning Gruell, Friederike Münn, Ralf Eggeling, Nico Pfeifer, N Han Le, Irmgard Landgraf, Florian Kurth, Leif E Sander, Florian Klein
  Durability of omicron-neutralising serum activity after mRNA booster immunisation in older adults
  The Lancet Infectious Diseases, 2022
• Felix Erne*, Daniel Dehncke*, Steven C. Herath, Fabian Springer, Nico Pfeifer, Ralf Eggeling**, Markus Alexander Küper**
  Deep Learning in the Detection of Rare Fractures – Development of a “Deep Learning Convolutional Network” Model for Detecting Acetabular Fractures
  *: shared authorship
  **: shared last authorship
  Zeitschrift für Orthopädie und Unfallchirurgie, 26 July 2021
• M. Korenkov, N. Poopalasingam, M. Madler, K. Vanshylla, R. Eggeling, M. Wirtz, I. Fish, F. Dewald, L. Gieselmann, C. Lehmann, G. Fätkenheuer, H. Gruell, N. Pfeifer, E. Heger, F. Klein
  Evaluation of a rapid antigen test to detect SARS-CoV-2 infection and identify potentially infectious individuals
  Journal of Clinical Microbiology, 02 July 2021
• K. Vanshylla, V. Di Cristanziano, F. Kleipass, F. Dewald, P. Schommers, L. Gieselmann, H. Gruell, M. Schlotz, M. S. Ercanoglu, R. Stumpf, P. Mayer, M. Zehner, E. Heger, W. Johannis, C. Horn, I. Suárez, N. Jung, S. Salomon, K. A. Eberhardt, B. Gathof, G. Fätkenheuer, N. Pfeifer, R. Eggeling, M. Augustin, C. Lehmann, F. Klein
  Kinetics and correlates of the neutralizing antibody response to SARS-CoV-2 infection in humans
  Cell Host & Microbe, 2021
• S. Käppel, R. Eggeling, F. Rümpler, M. Groth, R. Melzer, G. Theißen
  DNA-binding properties of the MADS-domain transcription factor SEPALLATA3 and mutant variants characterized by SELEX-seq
  Plant Molecular Biology, 2021
• S. Ehrhardt, M. Zehner, V. Krähling, H. Cohen-Dvashi, C. Kreer, N. Elad, H. Gruell, M.S. Ercanoglu, P. Schommers, L. Gieselmann, R. Eggeling, C. Dahlke, T. Wolf, N. Pfeifer, M. Addo, R. Diskin, S. Becker, F. Klein
  Polyclonal and convergent antibody response to Ebola virus vaccine rVSV-ZEBOV
  Nature Medicine, 2019
• T. Talvitie, R. Eggeling, M. Koivisto
  Learning Bayesian networks with local structure, mixed variables, and exact algorithms
  International Journal of Approximate Reasoning, 2019
• L. Handl, A. Jalali, M. Scherer, R. Eggeling, N. Pfeifer
  Weighted Elastic Net for Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation Data
  Bioinformatics, 2019
• R. Eggeling, J. Viinikka, A. Vuoksenmaa, M. Koivisto
  On Structure Priors for Learning Bayesian Networks
  AISTATS 2019
• R. Eggeling, I. Grosse, M. Koivisto
  Algorithms for learning parsimonious context trees
  Machine Learning, 2019
• T. Talvitie, R. Eggeling, M. Koivisto
  Finding Optimal Bayesian Networks with Local Structure
  PGM 2018
• R. Eggeling
  Disentangling transcription factor binding site complexity
  Nucleic Acids Research, 2018
• J. Viinikka, R. Eggeling, M. Koivisto
  Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth
  AISTATS 2018
• C. Mehlgarten, R. Eggeling, A. Gohr, M. Bönn, I. Lemnian, M. Nettling, K. Strödecke, C. Kleindienst, I. Grosse, K. Breunig
  Evolution of the AMP-Activated Protein Kinase Controlled Gene Regulatory Network
  In Information- and Communication Theory in Molecular Biology, 2018
• R. Eggeling, I. Grosse, J. Grau
  InMoDe: tools for learning and visualizing intra-motif dependencies of DNA binding sites
  Bioinformatics, 2017
• R. Eggeling, M. Koivisto
  Pruning Rules for Learning Parsimonious Context Trees
  UAI 2016
• R. Eggeling, T. Roos, P. Myllymäki, I. Grosse
  Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data
  BMC Bioinformatics, 2015
• R. Eggeling, M. Koivisto, I. Grosse
  Dealing with Small Data: On the Generalization of Context Trees
  ICML 2015
• R. Eggeling
  Learning inhomogeneous parsimonious Markov models with application to DNA sequence analysis
  Dissertation, Martin Luther University Halle–Wittenberg, 2014
• R. Eggeling, T. Roos, P. Myllymäki, I. Grosse
  Robust learning of inhomogeneous PMMs
  AISTATS 2014
• R. Eggeling, A. Gohr, J. Keilwagen, M. Mohr, S. Posch, A.D. Smith, I. Grosse
  On the Value of Intra-Motif Dependencies of Human Insulator Protein CTCF
  PLOS ONE, 2014
• R. Eggeling, A. Gohr, P.-Y. Bourguignon, E. Wingender, I. Grosse
  Inhomogeneous Parsimonious Markov Models
  ECML PKDD 2013
• I. M. Lemnian, R. Eggeling, I. Grosse
  Extended Sunflower Hidden Markov Models for the recognition of homotypic cis-regulatory modules
  GCB 2013
• R. Eggeling, P.-Y. Bourguignon, A. Gohr, I. Grosse
  Gibbs sampling for parsimonious Markov models with latent variables
  PGM 2012