Sune Pletscher-Frankild
- Molecular Biology top 1%
- Biomedical Text Mining and Ontologies 9
- vaccines and immunoinformatics approaches 6
- Bioinformatics and Genomic Networks 6
- Genomics and Phylogenetic Studies 2
- Machine Learning in Bioinformatics 1
- Cancer Research top 2%
- Immunology top 5%
- T-cell and B-cell Immunology 4
- Aging top 10%
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- Genomics and Rare Diseases 2
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- Influenza Virus Research Studies 1
- Co-authors
- Lars Juhl JensenPeer BorkDamian SzklarczykChristian von MeringMichael KuhnAndrea FranceschiniMilan SimonovicPablo Mínguez
- Partner nations
- DenmarkGermanyUnited States
In The Last Decade
Sune Pletscher-Frankild
17 papers receiving 6.0k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Molecular Biology 4.4k
- Cancer Research 800
- Immunology 919
- Computational Theory and Mathematics 563
- Aging 32
Countries citing papers authored by Sune Pletscher-Frankild
This map shows the geographic impact of Sune Pletscher-Frankild'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 Sune Pletscher-Frankild with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sune Pletscher-Frankild more than expected).
Fields of papers citing papers by Sune Pletscher-Frankild
This network shows the impact of papers produced by Sune Pletscher-Frankild. 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 Sune Pletscher-Frankild. The network helps show where Sune Pletscher-Frankild may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Sune Pletscher-Frankild, 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 | 2020 | 14 | |
| 2 | 2019 | 7 | |
| 3 | 2015 | 14 | |
| 4 | 2015 | 317 | |
| 5 | 2015 | 65 | |
| 6 | DISEASES: Text mining and data integration of disease–gene associationsbreakdown → | 2014 | 396 |
| 7 | COMPARTMENTS: unification and visualization of protein subcellular localization evidencebreakdown → | 2014 | 432 |
| 8 | 2013 | 64 | |
| 9 | 2013 | 121 | |
| 10 | STITCH 4: integration of protein–chemical interactions with user databreakdown → | 2013 | 362 |
| 11 | 2013 | 173 | |
| 12 | STRING v9.1: protein-protein interaction networks, with increased coverage and integrationbreakdown → | 2012 | 3505 |
| 13 | 2008 | 83 | |
| 14 | 2008 | 299 | |
| 15 | 2006 | 16 | |
| 16 | 2006 | 230 | |
| 17 | 2005 | 7 |
About Sune Pletscher-Frankild
Sune Pletscher-Frankild is a scholar working on Molecular Biology, Immunology and Ecological Modeling, having authored 17 papers that have together received 6.1k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (9 papers), vaccines and immunoinformatics approaches (6 papers), Bioinformatics and Genomic Networks (6 papers), T-cell and B-cell Immunology (4 papers), Genomics and Rare Diseases (2 papers), Genomics and Phylogenetic Studies (2 papers), Influenza Virus Research Studies (1 paper) and Machine Learning in Bioinformatics (1 paper). The work is most often cited by research in Molecular Biology (4.4k citations), Cancer Research (800 citations) and Immunology (919 citations). Sune Pletscher-Frankild has collaborated with scholars based in Denmark, Germany and United States. Frequent co-authors include Lars Juhl Jensen, Peer Bork, Damian Szklarczyk, Christian von Mering, Michael Kuhn, Andrea Franceschini, Milan Simonovic, Pablo Mínguez, Alexander Röth and Jianyi Lin. Their work appears in journals such as Nucleic Acids Research, PLoS ONE, PLoS Computational Biology, Bioinformatics and Methods.
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.