Ralf Eggeling
Impact in
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- SARS-CoV-2 and COVID-19 Research
- SARS-CoV-2 detection and testing
- Viral Infections and Outbreaks Research
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- Genomics and Chromatin Dynamics
- Gene expression and cancer classification
- RNA and protein synthesis mechanisms
Papers in
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- Gene expression and cancer classification 5
- Genomics and Chromatin Dynamics 4
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- Bayesian Modeling and Causal Inference 6
- Bayesian Methods and Mixture Models 2
- Algorithms and Data Compression 2
- Co-authors
- Ivo Große (6 shared papers)Mikko Koivisto (7 shared papers)Nico Pfeifer (6 shared papers)Teemu Roos (2 shared papers)Petri Myllymäki (2 shared papers)Henning Gruell (2 shared papers)Lutz Gieselmann (2 shared papers)Florian Klein (2 shared papers)
In The Last Decade
Ralf Eggeling
17 papers receiving 235 citations
Peers
Comparison fields: 5 of 62
- Infectious Diseases 86
- Molecular Biology 111
- Health Informatics 2
- Modeling and Simulation 6
- Artificial Intelligence 36
Countries citing papers authored by Ralf Eggeling
This map shows the geographic impact of Ralf Eggeling's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ralf Eggeling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ralf Eggeling more than expected).
Fields of papers citing papers by Ralf Eggeling
This network shows the impact of papers produced by Ralf Eggeling. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ralf Eggeling. The network helps show where Ralf Eggeling may publish in the future.
Co-authors
The 25 scholars most cited alongside Ralf Eggeling, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 65 | |
| 2 | 2021 | 43 | |
| 3 | 2015 | 27 | |
| 4 | 2014 | 19 | |
| 5 | 2019 | 16 | |
| 6 | 2016 | 13 | |
| 7 | 2018 | 12 | |
| 8 | 2021 | 8 | |
| 9 | 2019 | 7 | |
| 10 | Robust learning of inhomogeneous PMMs | 2014 | 5 |
| 11 | Dealing with small data: On the generalization of context trees | 2015 | 5 |
| 12 | 2021 | 5 | |
| 13 | Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth | 2018 | 5 |
| 14 | Finding Optimal Bayesian Networks with Local Structure. | 2018 | 3 |
| 15 | Pruning rules for learning parsimonious context trees | 2016 | 3 |
| 16 | On Structure Priors for Learning Bayesian Networks | 2019 | 2 |
| 17 | 2018 | 2 | |
| 18 | 2025 | 0 | |
| 19 | 2024 | 0 |
About Ralf Eggeling
Ralf Eggeling is a scholar working on Molecular Biology, Artificial Intelligence, Infectious Diseases, Information Systems and Management Science and Operations Research, having authored 19 papers that have together received 240 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (6 papers), Gene expression and cancer classification (5 papers), Genomics and Chromatin Dynamics (4 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Data Mining Algorithms and Applications (2 papers), Data Quality and Management (2 papers), Bayesian Methods and Mixture Models (2 papers) and Algorithms and Data Compression (2 papers). The work is most often cited by research in Infectious Diseases (86 citations), Molecular Biology (111 citations), Health Informatics (2 citations), Modeling and Simulation (6 citations) and Artificial Intelligence (36 citations). Ralf Eggeling has collaborated with scholars based in Germany, Finland and Slovakia. Frequent co-authors include Ivo Große, Mikko Koivisto, Nico Pfeifer, Teemu Roos, Petri Myllymäki, Henning Gruell, Lutz Gieselmann, Florian Klein, Ron Diskin and Philipp Schommers. Their work appears in journals such as Bioinformatics, Nature Medicine, International Journal of Approximate Reasoning, BMC Bioinformatics and Journal of Clinical Microbiology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.