Pierre Dönnes
Impact in
- Immunology top 10%
- Immunotherapy and Immune Responses
- T-cell and B-cell Immunology
- Molecular Biology top 10%
- vaccines and immunoinformatics approaches
- Machine Learning in Bioinformatics
- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
Papers in
- Immunology 18
- Immunotherapy and Immune Responses 12
- T-cell and B-cell Immunology 6
-
- Systemic Lupus Erythematosus Research 5
- Co-authors
- Oliver KohlbacherAnnette HöglundArne ElofssonTorsten BlumTassula Proikas‐CezanneZsuzsanna TakácsHans‐Werner AdolphElizabeth C. Jury
- Journals
- Bioinformatics (3 papers)Annals of the Rheumatic Diseases (3 papers)Cells (2 papers)PLoS Computational Biology (2 papers)PLoS ONE (2 papers)
- Partner nations
- GermanySwedenUnited Kingdom
In The Last Decade
Pierre Dönnes
37 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 119
- Immunology 348
- Molecular Biology 1.0k
- Radiology, Nuclear Medicine and Imaging 240
- Physiology 45
- Neurology 70
Countries citing papers authored by Pierre Dönnes
This map shows the geographic impact of Pierre Dönnes'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 Pierre Dönnes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pierre Dönnes more than expected).
Fields of papers citing papers by Pierre Dönnes
This network shows the impact of papers produced by Pierre Dönnes. 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 Pierre Dönnes. The network helps show where Pierre Dönnes may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pierre Dönnes, 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 | 2024 | 23 | |
| 2 | 2024 | 5 | |
| 3 | 2024 | 12 | |
| 4 | 2024 | 2 | |
| 5 | 2023 | 15 | |
| 6 | 2022 | 2 | |
| 7 | 2021 | 25 | |
| 8 | 2021 | 76 | |
| 9 | 2021 | 5 | |
| 10 | 2020 | 29 | |
| 11 | 2019 | 13 | |
| 12 | 2018 | 17 | |
| 13 | 2018 | 20 | |
| 14 | 2014 | 13 | |
| 15 | 2012 | 4 | |
| 16 | 2008 | 32 | |
| 17 | 2007 | 3 | |
| 18 | 2007 | 92 | |
| 19 | Using N-terminal targeting sequences, amino acid composition, and sequence motifs for predicting protein subcellular localizations. | 2005 | 2 |
| 20 | 2005 | 62 |
About Pierre Dönnes
Pierre Dönnes is a scholar working on Immunology, Rheumatology, Nephrology, Hematology and Radiology, Nuclear Medicine and Imaging, having authored 37 papers that have together received 1.5k indexed citations. Recurring topics across this work include Immunotherapy and Immune Responses (12 papers), vaccines and immunoinformatics approaches (8 papers), Machine Learning in Bioinformatics (7 papers), T-cell and B-cell Immunology (6 papers), Monoclonal and Polyclonal Antibodies Research (6 papers), Genomics and Phylogenetic Studies (5 papers), Systemic Lupus Erythematosus Research (5 papers) and RNA and protein synthesis mechanisms (5 papers). The work is most often cited by research in Immunology (348 citations), Molecular Biology (1.0k citations), Radiology, Nuclear Medicine and Imaging (240 citations), Physiology (45 citations) and Neurology (70 citations). Pierre Dönnes has collaborated with scholars based in Germany, Sweden and United Kingdom. Frequent co-authors include Oliver Kohlbacher, Annette Höglund, Arne Elofsson, Torsten Blum, Tassula Proikas‐Cezanne, Zsuzsanna Takács, Hans‐Werner Adolph, Elizabeth C. Jury, Junjie Peng and Coziana Ciurtin. Their work appears in journals such as Bioinformatics, Annals of the Rheumatic Diseases, Cells, PLoS Computational Biology and PLoS ONE.
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.