Peter Hönigschmid
Impact in
- Molecular Biology top 5%
- Machine Learning in Bioinformatics
- Bioinformatics and Genomic Networks
- Protein Structure and Dynamics
- Genomics and Phylogenetic Studies
- Biomedical Text Mining and Ontologies
Papers in
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- Machine Learning in Bioinformatics 5
- Protein Structure and Dynamics 4
- Genomics and Phylogenetic Studies 3
- Bioinformatics and Genomic Networks 2
- Genetics 2
- Genomics and Rare Diseases 1
- Bacterial Genetics and Biotechnology 1
- Co-authors
- Burkhard Rost (3 shared papers)Nir Ben‐Tal (2 shared papers)Tobias Hamp (2 shared papers)Manfred Roos (2 shared papers)Maximilian Hecht (2 shared papers)Guy Yachdav (1 shared paper)Haim Ashkenazy (1 shared paper)Avner Schlessinger (1 shared paper)
- Journals
- BMC Bioinformatics (2 papers)Journal of Structural Biology (2 papers)Nucleic Acids Research (2 papers)Genome Biology and Evolution (1 paper)Bioinformatics (1 paper)
- Partner nations
- GermanyRussiaUnited States
In The Last Decade
Peter Hönigschmid
10 papers receiving 1.5k citations
Peter Hönigschmid's Hit Papers
Peers
Comparison fields: 5 of 105
- Molecular Biology 1k
- Health Informatics 17
- Genetics 115
- Endocrinology 13
- Microbiology 17
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 | A large-scale evaluation of computational protein function prediction Hit paper breakdown → | 2013 | 757 |
| 2 | 2014 | 487 | |
| 3 | 2013 | 57 | |
| 4 | 2020 | 55 | |
| 5 | 2018 | 33 | |
| 6 | 2013 | 27 | |
| 7 | 2016 | 24 | |
| 8 | 2019 | 16 | |
| 9 | 2018 | 9 | |
| 10 | 2020 | 3 |
About Peter Hönigschmid
Peter Hönigschmid is a scholar working on Molecular Biology, Genetics, Information Systems and Management, Insect Science and Spectroscopy, having authored 10 papers that have together received 1.5k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (5 papers), Protein Structure and Dynamics (4 papers), Genomics and Phylogenetic Studies (3 papers), Bioinformatics and Genomic Networks (2 papers), Genomics and Rare Diseases (1 paper), Insect symbiosis and bacterial influences (1 paper), Bacterial Genetics and Biotechnology (1 paper) and Advanced Proteomics Techniques and Applications (1 paper). The work is most often cited by research in Molecular Biology (1k citations), Health Informatics (17 citations), Genetics (115 citations), Endocrinology (13 citations) and Microbiology (17 citations). Peter Hönigschmid has collaborated with scholars based in Germany, Russia and United States. Frequent co-authors include Burkhard Rost, Nir Ben‐Tal, Tobias Hamp, Manfred Roos, Maximilian Hecht, Guy Yachdav, Haim Ashkenazy, Avner Schlessinger, Michael Bernhofer and Andrea Schafferhans. Their work appears in journals such as BMC Bioinformatics, Journal of Structural Biology, Nucleic Acids Research, Genome Biology and Evolution and Bioinformatics.
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.