Éva Schád
Impact in
- Molecular Biology top 5%
- Protein Structure and Dynamics
- RNA Research and Splicing
- Machine Learning in Bioinformatics
- Genomics and Chromatin Dynamics
- Genomics and Phylogenetic Studies
- Bioinformatics and Genomic Networks
- RNA and protein synthesis mechanisms
- Cell Biology top 10%
- Calpain Protease Function and Regulation
Papers in
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- Protein Structure and Dynamics 18
- RNA Research and Splicing 6
- Genomics and Chromatin Dynamics 3
- Epigenetics and DNA Methylation 1
- Heat shock proteins research 1
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- Calpain Protease Function and Regulation 5
- Co-authors
- Péter Tompa (17 shared papers)Ágnes Tantos (16 shared papers)Hédi Hegyi (2 shared papers)Lajos Kalmár (4 shared papers)Rita Pancsa (4 shared papers)Bálint Mészáros (3 shared papers)Zsuzsanna Dosztányi (3 shared papers)Beáta Szabó (8 shared papers)
In The Last Decade
Éva Schád
32 papers receiving 2k citations
Éva Schád's Hit Papers
Peers
Comparison fields: 5 of 80
- Molecular Biology 1.7k
- Cell Biology 135
- Aging 5
- Genetics 79
- Spectroscopy 31
Countries citing papers authored by Éva Schád
This map shows the geographic impact of Éva Schád'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 Éva Schád with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Éva Schád more than expected).
Fields of papers citing papers by Éva Schád
This network shows the impact of papers produced by Éva Schád. 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 Éva Schád. The network helps show where Éva Schád may publish in the future.
Co-authors
The 25 scholars most cited alongside Éva Schád, 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 32 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Critical assessment of protein intrinsic disorder prediction Hit paper breakdown → | 2021 | 251 |
| 2 | 2016 | 242 | |
| 3 | 2015 | 183 | |
| 4 | 2019 | 183 | |
| 5 | 2011 | 165 | |
| 6 | 2017 | 161 | |
| 7 | 2019 | 156 | |
| 8 | 2021 | 128 | |
| 9 | 2007 | 80 | |
| 10 | 2017 | 73 | |
| 11 | 2019 | 53 | |
| 12 | 1996 | 48 | |
| 13 | 2022 | 42 | |
| 14 | 2002 | 37 | |
| 15 | 1995 | 20 | |
| 16 | 2025 | 20 | |
| 17 | 2004 | 18 | |
| 18 | 2016 | 18 | |
| 19 | 2021 | 16 | |
| 20 | 2022 | 16 |
About Éva Schád
Éva Schád is a scholar working on Molecular Biology, Cell Biology, Plant Science, Biomaterials and Cancer Research, having authored 32 papers that have together received 2k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (18 papers), RNA Research and Splicing (6 papers), Calpain Protease Function and Regulation (5 papers), Genomics and Chromatin Dynamics (3 papers), Chromosomal and Genetic Variations (2 papers), Epigenetics and DNA Methylation (1 paper), Heat shock proteins research (1 paper) and Cancer Cells and Metastasis (1 paper). The work is most often cited by research in Molecular Biology (1.7k citations), Cell Biology (135 citations), Aging (5 citations), Genetics (79 citations) and Spectroscopy (31 citations). Éva Schád has collaborated with scholars based in Hungary, Belgium and Italy. Frequent co-authors include Péter Tompa, Ágnes Tantos, Hédi Hegyi, Lajos Kalmár, Rita Pancsa, Bálint Mészáros, Zsuzsanna Dosztányi, Beáta Szabó, Peter Friedrich and Tamás Horváth. Their work appears in journals such as Nucleic Acids Research, International Journal of Molecular Sciences, Protein Science, Biochemical Journal and Biomolecules.
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.