Philip Bucher
Impact in
- Molecular Biology top 5%
- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
- Machine Learning in Bioinformatics
- Protein Structure and Dynamics
- Glycosylation and Glycoproteins Research
- Bioinformatics and Genomic Networks
- Immunology top 10%
Papers in
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- NF-κB Signaling Pathways 3
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- Immune Response and Inflammation 2
- Immune Cell Function and Interaction 2
- Co-authors
- Amos BairochKay HofmannLaurent FalquetKlaus Schulze‐OsthoffStephan HailfingerAnja SchmittDaniela KramerJessica Löffler
- Journals
- Nucleic Acids Research (2 papers)Journal of Investigative Dermatology (1 paper)BMC Cancer (1 paper)Blood Advances (1 paper)Biomedicines (1 paper)
- Partner nations
- GermanySwitzerlandSlovakia
In The Last Decade
Philip Bucher
11 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 116
- Molecular Biology 2.0k
- Immunology 280
- Aging 21
- Cell Biology 173
- Biotechnology 88
Countries citing papers authored by Philip Bucher
This map shows the geographic impact of Philip Bucher'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 Philip Bucher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip Bucher more than expected).
Fields of papers citing papers by Philip Bucher
This network shows the impact of papers produced by Philip Bucher. 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 Philip Bucher. The network helps show where Philip Bucher may publish in the future.
Co-authors
The 25 scholars most cited alongside Philip Bucher, 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 | 2025 | 0 | |
| 2 | 2023 | 8 | |
| 3 | 2020 | 6 | |
| 4 | 2019 | 27 | |
| 5 | 2019 | 30 | |
| 6 | 2018 | 41 | |
| 7 | 2014 | 12 | |
| 8 | The PROSITE database, its status in 1999 Hit paper breakdown → | 1999 | 936 |
| 9 | The PROSITE database, its status in 1997 Hit paper breakdown → | 1997 | 1548 |
| 10 | [Description of eukaryotic promoters in the EPD database]. | 1997 | 4 |
| 11 | A sequence similarity search algorithm based on a probabilistic interpretation of an alignment scoring system. | 1996 | 33 |
| 12 | A generalized profile syntax for biomolecular sequence motifs and its function in automatic sequence interpretation. | 1994 | 132 |
About Philip Bucher
Philip Bucher is a scholar working on Cancer Research, Immunology, Microbiology, Pathology and Forensic Medicine and Molecular Biology, having authored 12 papers that have together received 2.8k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (4 papers), Genomics and Phylogenetic Studies (4 papers), NF-κB Signaling Pathways (3 papers), RNA and protein synthesis mechanisms (3 papers), Immune Response and Inflammation (2 papers), Lymphoma Diagnosis and Treatment (2 papers), Immune Cell Function and Interaction (2 papers) and Manufacturing Process and Optimization (1 paper). The work is most often cited by research in Molecular Biology (2.0k citations), Immunology (280 citations), Aging (21 citations), Cell Biology (173 citations) and Biotechnology (88 citations). Philip Bucher has collaborated with scholars based in Germany, Switzerland and Slovakia. Frequent co-authors include Amos Bairoch, Kay Hofmann, Laurent Falquet, Klaus Schulze‐Osthoff, Stephan Hailfinger, Anja Schmitt, Daniela Kramer, Jessica Löffler, Ari Waisman and Knut Schäkel. Their work appears in journals such as Nucleic Acids Research, Journal of Investigative Dermatology, BMC Cancer, Blood Advances and Biomedicines.
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.