Peter Hönigschmid
Impact in
- Health Informatics top 10%
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- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Protein Structure and Dynamics
- Genomics and Phylogenetic Studies
- Bioinformatics and Genomic Networks
- Photosynthetic Processes and Mechanisms
Papers in
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- Machine Learning in Bioinformatics 6
- Protein Structure and Dynamics 4
- Bioinformatics and Genomic Networks 3
- RNA and protein synthesis mechanisms 2
- Genomics and Phylogenetic Studies 1
- S100 Proteins and Annexins 1
- Co-authors
- Burkhard Rost (4 shared papers)Tobias Hamp (2 shared papers)Maximilian Hecht (2 shared papers)Nir Ben‐Tal (2 shared papers)Chris Sander (1 shared paper)László Kaján (1 shared paper)Haim Ashkenazy (1 shared paper)Guy Yachdav (1 shared paper)
- Journals
- Nucleic Acids Research (2 papers)BMC Bioinformatics (2 papers)Journal of Structural Biology (2 papers)Bioinformatics (1 paper)Genome Biology and Evolution (1 paper)
- Partner nations
- GermanyRussiaUnited States
In The Last Decade
Peter Hönigschmid
9 papers receiving 680 citations
Peter Hönigschmid's Hit Papers
Peers
Comparison fields: 5 of 117
- Health Informatics 12
- Molecular Biology 420
- Microbiology 20
- Endocrinology 15
- Cell Biology 45
Countries citing papers authored by Peter Hönigschmid
This map shows the geographic impact of Peter Hönigschmid'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 Peter Hönigschmid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Hönigschmid more than expected).
Fields of papers citing papers by Peter Hönigschmid
This network shows the impact of papers produced by Peter Hönigschmid. 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 Peter Hönigschmid. The network helps show where Peter Hönigschmid may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter Hönigschmid, 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 | PredictProtein—an open resource for online prediction of protein structural and functional features Hit paper breakdown → | 2014 | 475 |
| 2 | 2013 | 56 | |
| 3 | 2020 | 50 | |
| 4 | 2018 | 31 | |
| 5 | 2016 | 24 | |
| 6 | 2013 | 23 | |
| 7 | 2019 | 15 | |
| 8 | 2018 | 8 | |
| 9 | 2020 | 3 | |
| 10 | 2019 | 0 |
About Peter Hönigschmid
Peter Hönigschmid is a scholar working on Molecular Biology, Pharmacology, Ecology, Computational Theory and Mathematics and Genetics, having authored 10 papers that have together received 685 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (6 papers), Protein Structure and Dynamics (4 papers), Bioinformatics and Genomic Networks (3 papers), RNA and protein synthesis mechanisms (2 papers), Advanced Proteomics Techniques and Applications (1 paper), Computational Drug Discovery Methods (1 paper), Genomics and Phylogenetic Studies (1 paper) and S100 Proteins and Annexins (1 paper). The work is most often cited by research in Health Informatics (12 citations), Molecular Biology (420 citations), Microbiology (20 citations), Endocrinology (15 citations) and Cell Biology (45 citations). Peter Hönigschmid has collaborated with scholars based in Germany, Russia and United States. Frequent co-authors include Burkhard Rost, Tobias Hamp, Maximilian Hecht, Nir Ben‐Tal, Chris Sander, László Kaján, Haim Ashkenazy, Guy Yachdav, Marco Punta and Reinhard Schneider. Their work appears in journals such as Nucleic Acids Research, BMC Bioinformatics, Journal of Structural Biology, Bioinformatics and Genome Biology and Evolution.
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.