Johanna Sápi
Impact in
- Modeling and Simulation top 1%
- Mathematical Biology Tumor Growth
- Computational Mathematics top 10%
Papers in
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- Angiogenesis and VEGF in Cancer 23
- PI3K/AKT/mTOR signaling in cancer 3
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- Mathematical Biology Tumor Growth 29
- Co-authors
- Levente Kovács (36 shared papers)Dániel András Drexler (29 shared papers)Zoltàn Sápi (13 shared papers)István Harmati (16 shared papers)Pál Kocsis (1 shared paper)Zoltán Benyó (2 shared papers)G Papp (4 shared papers)Imre J. Rudas (2 shared papers)
- Journals
- Acta Polytechnica Hungarica (2 papers)Optimal Control Applications and Methods (1 paper)Computer Methods and Programs in Biomedicine (1 paper)Journal of Translational Medicine (1 paper)PLoS ONE (1 paper)
- Partner nations
- HungaryNetherlandsRomania
In The Last Decade
Johanna Sápi
41 papers receiving 636 citations
Peers
Comparison fields: 5 of 86
- Modeling and Simulation 324
- Computational Mathematics 12
- Cancer Research 141
- Oncology 161
- Molecular Biology 280
Countries citing papers authored by Johanna Sápi
This map shows the geographic impact of Johanna Sápi'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 Johanna Sápi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johanna Sápi more than expected).
Fields of papers citing papers by Johanna Sápi
This network shows the impact of papers produced by Johanna Sápi. 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 Johanna Sápi. The network helps show where Johanna Sápi may publish in the future.
Co-authors
The 20 scholars most cited alongside Johanna Sápi, 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 44 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 127 | |
| 2 | 2017 | 52 | |
| 3 | 2014 | 46 | |
| 4 | 2011 | 43 | |
| 5 | 2012 | 37 | |
| 6 | 2017 | 28 | |
| 7 | 2017 | 26 | |
| 8 | 2012 | 23 | |
| 9 | 2015 | 22 | |
| 10 | 2021 | 19 | |
| 11 | 2012 | 19 | |
| 12 | 2018 | 17 | |
| 13 | 2017 | 15 | |
| 14 | 2017 | 15 | |
| 15 | 2017 | 14 | |
| 16 | 2012 | 14 | |
| 17 | 2013 | 14 | |
| 18 | 2017 | 12 | |
| 19 | 2013 | 12 | |
| 20 | 2015 | 9 |
About Johanna Sápi
Johanna Sápi is a scholar working on Molecular Biology, Modeling and Simulation, Cancer Research, Oncology and Pulmonary and Respiratory Medicine, having authored 44 papers that have together received 646 indexed citations. Recurring topics across this work include Mathematical Biology Tumor Growth (29 papers), Angiogenesis and VEGF in Cancer (23 papers), Cancer, Hypoxia, and Metabolism (18 papers), Cancer Cells and Metastasis (5 papers), Sarcoma Diagnosis and Treatment (3 papers), Computational Drug Discovery Methods (3 papers), PI3K/AKT/mTOR signaling in cancer (3 papers) and Pancreatic function and diabetes (3 papers). The work is most often cited by research in Modeling and Simulation (324 citations), Computational Mathematics (12 citations), Cancer Research (141 citations), Oncology (161 citations) and Molecular Biology (280 citations). Johanna Sápi has collaborated with scholars based in Hungary, Netherlands and Romania. Frequent co-authors include Levente Kovács, Dániel András Drexler, Zoltàn Sápi, István Harmati, Pál Kocsis, Zoltán Benyó, G Papp, Imre J. Rudas, Bernadett Kiss and Péter Tátrai. Their work appears in journals such as Acta Polytechnica Hungarica, Optimal Control Applications and Methods, Computer Methods and Programs in Biomedicine, Journal of Translational Medicine 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.