David DeCaprio
Impact in
- Microbiology top 5%
- Microbial infections and disease research
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- Computational Drug Discovery Methods
Papers in
-
- Genomics and Phylogenetic Studies 3
- Machine Learning in Bioinformatics 2
- Genetics, Bioinformatics, and Biomedical Research 1
- Plant biochemistry and biosynthesis 1
- Fungal and yeast genetics research 1
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- Computational Drug Discovery Methods 2
- Co-authors
- Lakshmi B. Akella (1 shared paper)James E. Galagan (4 shared papers)Bruce W. Birren (2 shared papers)Sheila Fisher (1 shared paper)Shunguang Wang (1 shared paper)Robert Nicol (1 shared paper)Chad Nusbaum (1 shared paper)George M. Church (1 shared paper)
- Journals
- Genome Research (2 papers)Genetics (1 paper)Bioinformatics (1 paper)Current Protocols in Bioinformatics (1 paper)PLoS Computational Biology (1 paper)
- Partner nations
- United States
In The Last Decade
David DeCaprio
8 papers receiving 462 citations
Peers
Comparison fields: 5 of 79
- Microbiology 108
- Computational Theory and Mathematics 71
- Aging 7
- Molecular Biology 244
- Ecology 91
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 | 208 | |
| 2 | 2010 | 82 | |
| 3 | 2008 | 53 | |
| 4 | 2007 | 53 | |
| 5 | 2006 | 36 | |
| 6 | 2011 | 35 | |
| 7 | 2008 | 7 | |
| 8 | 2007 | 3 |
About David DeCaprio
David DeCaprio is a scholar working on Molecular Biology, Computational Theory and Mathematics, Pharmacology, Ecology and Oncology, having authored 8 papers that have together received 477 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (3 papers), Machine Learning in Bioinformatics (2 papers), Computational Drug Discovery Methods (2 papers), Aquaculture disease management and microbiota (1 paper), Genetic diversity and population structure (1 paper), Genetics, Bioinformatics, and Biomedical Research (1 paper), Plant biochemistry and biosynthesis (1 paper) and Fungal and yeast genetics research (1 paper). The work is most often cited by research in Microbiology (108 citations), Computational Theory and Mathematics (71 citations), Aging (7 citations), Molecular Biology (244 citations) and Ecology (91 citations). David DeCaprio has collaborated with scholars based in United States. Frequent co-authors include Lakshmi B. Akella, James E. Galagan, Bruce W. Birren, Sheila Fisher, Shunguang Wang, Robert Nicol, Chad Nusbaum, George M. Church, Jane Wilkinson and Jonathan A. Butler. Their work appears in journals such as Genome Research, Genetics, Bioinformatics, Current Protocols in Bioinformatics and PLoS Computational 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.