Dr. Ralf Eggeling
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
- Winter 2022/23
- Seminar: Machine Learning for Health
- Proseminar: Grundlagen der Medizininformatik
- Summer 2022
- Summer 2021
- Winter 2020/21
- Seminar: Machine Learning for Health
- Summer 2020
- Winter 2019/20
- Seminar: Machine Learning for Health
- Summer 2019
- Winter 2018/19
- Seminar: Machine Learning for Health (with Nico Pfeifer)
- 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
