Peter Hönigschmid
Impact in
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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
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 3
- Genetics, Bioinformatics, and Biomedical Research 1
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- Computational Drug Discovery Methods 2
- Co-authors
- Burkhard Rost (3 shared papers)Tobias Hamp (2 shared papers)Maximilian Hecht (2 shared papers)Nir Ben‐Tal (2 shared papers)Yana Bromberg (1 shared paper)Guy Yachdav (1 shared paper)Avner Schlessinger (1 shared paper)Edda Kloppmann (1 shared paper)
- Journals
- Journal of Structural Biology (2 papers)Nucleic Acids Research (2 papers)BMC Bioinformatics (2 papers)Bioinformatics (1 paper)Genome Biology and Evolution (1 paper)
- Partner nations
- GermanyRussiaUnited States
In The Last Decade
Peter Hönigschmid
9 papers receiving 645 citations
Peter Hönigschmid's Hit Papers
Peers
Comparison fields: 5 of 115
- Health Informatics 11
- Molecular Biology 435
- Microbiology 23
- Endocrinology 16
- Genetics 83
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 | 455 |
| 2 | 2013 | 51 | |
| 3 | 2020 | 45 | |
| 4 | 2018 | 28 | |
| 5 | 2013 | 23 | |
| 6 | 2016 | 23 | |
| 7 | 2019 | 15 | |
| 8 | 2018 | 8 | |
| 9 | 2020 | 2 |
About Peter Hönigschmid
Peter Hönigschmid is a scholar working on Molecular Biology, Computational Theory and Mathematics, Genetics, Pharmacology and Ecology, having authored 9 papers that have together received 650 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 (3 papers), Computational Drug Discovery Methods (2 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper), Cell Image Analysis Techniques (1 paper) and Genomics and Rare Diseases (1 paper). The work is most often cited by research in Health Informatics (11 citations), Molecular Biology (435 citations), Microbiology (23 citations), Endocrinology (16 citations) and Genetics (83 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, Yana Bromberg, Guy Yachdav, Avner Schlessinger, Edda Kloppmann, Haim Ashkenazy and Reinhard Schneider. Their work appears in journals such as Journal of Structural Biology, Nucleic Acids Research, BMC Bioinformatics, 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.