Alexander Ullrich
Impact in
- Cell Biology top 5%
- Cellular transport and secretion
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
Papers in
-
- Protein Structure and Dynamics 4
- RNA and protein synthesis mechanisms 3
- Lipid Membrane Structure and Behavior 3
-
- Data-Driven Disease Surveillance 6
- Co-authors
- Frank Noé (5 shared papers)Johannes Schöneberg (5 shared papers)York Posor (2 shared papers)Volker Haucke (2 shared papers)Jan Schmoranzer (2 shared papers)Michaela Diercke (7 shared papers)André Lampe (1 shared paper)Emilio Hirsch (1 shared paper)
- Journals
- Eurosurveillance (3 papers)PLoS Computational Biology (3 papers)Nature Communications (1 paper)BMJ Open (1 paper)Frontiers in Microbiology (1 paper)
- Partner nations
- GermanyUnited StatesAustria
In The Last Decade
Alexander Ullrich
31 papers receiving 897 citations
Peers
Comparison fields: 5 of 119
- Cell Biology 309
- Modeling and Simulation 45
- Physiology 35
- Molecular Biology 482
- Biophysics 36
Countries citing papers authored by Alexander Ullrich
This map shows the geographic impact of Alexander Ullrich'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 Alexander Ullrich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Ullrich more than expected).
Fields of papers citing papers by Alexander Ullrich
This network shows the impact of papers produced by Alexander Ullrich. 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 Alexander Ullrich. The network helps show where Alexander Ullrich may publish in the future.
Co-authors
The 25 scholars most cited alongside Alexander Ullrich, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 315 | |
| 2 | 2021 | 132 | |
| 3 | 2017 | 83 | |
| 4 | 1986 | 81 | |
| 5 | 2022 | 56 | |
| 6 | 2015 | 52 | |
| 7 | 2014 | 50 | |
| 8 | 2015 | 24 | |
| 9 | 2019 | 18 | |
| 10 | 2020 | 17 | |
| 11 | 2010 | 11 | |
| 12 | 2023 | 10 | |
| 13 | 2022 | 10 | |
| 14 | 2011 | 8 | |
| 15 | 2024 | 6 | |
| 16 | 2018 | 4 | |
| 17 | 2021 | 4 | |
| 18 | 2022 | 4 | |
| 19 | 2016 | 4 | |
| 20 | 2009 | 4 |
About Alexander Ullrich
Alexander Ullrich is a scholar working on Molecular Biology, Epidemiology, Modeling and Simulation, General Health Professions and Immunology, having authored 34 papers that have together received 914 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (7 papers), Data-Driven Disease Surveillance (6 papers), Health and Medical Studies (5 papers), Protein Structure and Dynamics (4 papers), Cellular transport and secretion (3 papers), Immune responses and vaccinations (3 papers), RNA and protein synthesis mechanisms (3 papers) and Lipid Membrane Structure and Behavior (3 papers). The work is most often cited by research in Cell Biology (309 citations), Modeling and Simulation (45 citations), Physiology (35 citations), Molecular Biology (482 citations) and Biophysics (36 citations). Alexander Ullrich has collaborated with scholars based in Germany, United States and Austria. Frequent co-authors include Frank Noé, Johannes Schöneberg, York Posor, Volker Haucke, Jan Schmoranzer, Michaela Diercke, André Lampe, Emilio Hirsch, Rainer Müller and Federico Gulluni. Their work appears in journals such as Eurosurveillance, PLoS Computational Biology, Nature Communications, BMJ Open and Frontiers in 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.