Kyle Burkhardt
Impact in
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- Computational Drug Discovery Methods
- Molecular Biology top 5%
- Protein Structure and Dynamics
- Machine Learning in Bioinformatics
- Bioinformatics and Genomic Networks
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
Papers in
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- Enzyme Structure and Function 7
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- Research Data Management Practices 1
- Co-authors
- Helen Miriam Berman (6 shared papers)Bohdan Schneider (2 shared papers)John Westbrook (3 shared papers)Zukang Feng (2 shared papers)Wolfgang F. Bluhm (3 shared papers)Narmada Thanki (2 shared papers)Philip E. Bourne (2 shared papers)Talapady N. Bhat (2 shared papers)
- Journals
- Current Protocols in Bioinformatics (1 paper)Methods in enzymology on CD-ROM/Methods in enzymology (1 paper)PLoS Computational Biology (1 paper)Molecular Biotechnology (1 paper)Methods in molecular biology (1 paper)
- Partner nations
- United StatesUnited KingdomJapan
In The Last Decade
Kyle Burkhardt
7 papers receiving 2.3k citations
Kyle Burkhardt's Hit Papers
Peers
Comparison fields: 5 of 116
- Computational Theory and Mathematics 538
- Molecular Biology 1.2k
- Materials Chemistry 281
- Structural Biology 12
- Organic Chemistry 134
Countries citing papers authored by Kyle Burkhardt
This map shows the geographic impact of Kyle Burkhardt'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 Kyle Burkhardt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyle Burkhardt more than expected).
Fields of papers citing papers by Kyle Burkhardt
This network shows the impact of papers produced by Kyle Burkhardt. 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 Kyle Burkhardt. The network helps show where Kyle Burkhardt may publish in the future.
Co-authors
The 20 scholars most cited alongside Kyle Burkhardt, 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 | The Protein Data Bank Hit paper breakdown → | 2002 | 2172 |
| 2 | 2008 | 100 | |
| 3 | 2003 | 41 | |
| 4 | 2006 | 36 | |
| 5 | 2008 | 18 | |
| 6 | The Protein Data Bank (PDB) | NIST | 2002 | 4 |
| 7 | 2005 | 2 |
About Kyle Burkhardt
Kyle Burkhardt is a scholar working on Materials Chemistry, Information Systems, Molecular Biology, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 7 papers that have together received 2.4k indexed citations. Recurring topics across this work include Enzyme Structure and Function (7 papers), Biomedical Text Mining and Ontologies (1 paper) and Research Data Management Practices (1 paper). The work is most often cited by research in Computational Theory and Mathematics (538 citations), Molecular Biology (1.2k citations), Materials Chemistry (281 citations), Structural Biology (12 citations) and Organic Chemistry (134 citations). Kyle Burkhardt has collaborated with scholars based in United States, United Kingdom and Japan. Frequent co-authors include Helen Miriam Berman, Bohdan Schneider, John Westbrook, Zukang Feng, Wolfgang F. Bluhm, Narmada Thanki, Philip E. Bourne, Talapady N. Bhat, Helge Weissig and Lisa E. Iype. Their work appears in journals such as Current Protocols in Bioinformatics, Methods in enzymology on CD-ROM/Methods in enzymology, PLoS Computational Biology, Molecular Biotechnology and Methods in molecular biology.
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.