David DeCaprio
Impact in
- Microbiology top 5%
- Microbial infections and disease research
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- Computational Drug Discovery Methods
Papers in
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- Genomics and Phylogenetic Studies 2
- Genetics, Bioinformatics, and Biomedical Research 1
- Machine Learning in Bioinformatics 1
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- Computational Drug Discovery Methods 2
- Co-authors
- Lakshmi B. Akella (1 shared paper)James E. Galagan (3 shared papers)Bruce W. Birren (2 shared papers)Sarah E. Calvo (1 shared paper)Sheila Fisher (1 shared paper)Jacob D. Jaffe (1 shared paper)Robert Nicol (1 shared paper)Michael G. FitzGerald (1 shared paper)
- Journals
- Genome Research (2 papers)Current Protocols in Bioinformatics (1 paper)PLoS Computational Biology (1 paper)Diabetes (1 paper)Current Opinion in Chemical Biology (1 paper)
- Partner nations
- United States
In The Last Decade
David DeCaprio
8 papers receiving 429 citations
Peers
Comparison fields: 5 of 84
- Microbiology 104
- Computational Theory and Mathematics 64
- Aging 7
- Ecology 101
- Molecular Biology 246
Countries citing papers authored by David DeCaprio
This map shows the geographic impact of David DeCaprio'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 David DeCaprio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David DeCaprio more than expected).
Fields of papers citing papers by David DeCaprio
This network shows the impact of papers produced by David DeCaprio. 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 David DeCaprio. The network helps show where David DeCaprio may publish in the future.
Co-authors
The 25 scholars most cited alongside David DeCaprio, 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 | 2004 | 201 | |
| 2 | 2010 | 73 | |
| 3 | 2007 | 47 | |
| 4 | 2008 | 46 | |
| 5 | 2006 | 33 | |
| 6 | 2011 | 32 | |
| 7 | 2008 | 6 | |
| 8 | 2023 | 1 |
About David DeCaprio
David DeCaprio is a scholar working on Molecular Biology, Computational Theory and Mathematics, Plant Science, Pharmacology and General Health Professions, having authored 8 papers that have together received 439 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (2 papers), Genomics and Phylogenetic Studies (2 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper), Machine Learning in Bioinformatics (1 paper), Cytokine Signaling Pathways and Interactions (1 paper), Plant-Microbe Interactions and Immunity (1 paper), Rheumatoid Arthritis Research and Therapies (1 paper) and Microbial Natural Products and Biosynthesis (1 paper). The work is most often cited by research in Microbiology (104 citations), Computational Theory and Mathematics (64 citations), Aging (7 citations), Ecology (101 citations) and Molecular Biology (246 citations). David DeCaprio has collaborated with scholars based in United States. Frequent co-authors include Lakshmi B. Akella, James E. Galagan, Bruce W. Birren, Sarah E. Calvo, Sheila Fisher, Jacob D. Jaffe, Robert Nicol, Michael G. FitzGerald, Jonathan A. Butler and Howard C. Berg. Their work appears in journals such as Genome Research, Current Protocols in Bioinformatics, PLoS Computational Biology, Diabetes and Current Opinion in Chemical 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.