Phillip E. C. Compeau
Impact in
-
- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
- DNA and Biological Computing
- Machine Learning in Bioinformatics
- Artificial Intelligence top 10%
- Algorithms and Data Compression
Papers in
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- Genetics, Bioinformatics, and Biomedical Research 2
- Genomics and Phylogenetic Studies 2
- DNA and Biological Computing 2
- Machine Learning in Bioinformatics 1
- Genetics 3
- Genome Rearrangement Algorithms 3
- Co-authors
- Pavel A. Pevzner (3 shared papers)Glenn Tesler (1 shared paper)Laurie J. Heyer (1 shared paper)
- Journals
- Discrete Applied Mathematics (1 paper)Algorithms for Molecular Biology (1 paper)PLoS Computational Biology (1 paper)Nature Biotechnology (1 paper)Communications of the ACM (1 paper)
- Partner nations
- United States
In The Last Decade
Phillip E. C. Compeau
7 papers receiving 433 citations
Peers
Comparison fields: 5 of 90
- Molecular Biology 325
- Artificial Intelligence 96
- Plant Science 93
- Genetics 70
- Computational Theory and Mathematics 32
Countries citing papers authored by Phillip E. C. Compeau
This map shows the geographic impact of Phillip E. C. Compeau'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 Phillip E. C. Compeau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Phillip E. C. Compeau more than expected).
Fields of papers citing papers by Phillip E. C. Compeau
This network shows the impact of papers produced by Phillip E. C. Compeau. 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 Phillip E. C. Compeau. The network helps show where Phillip E. C. Compeau may publish in the future.
Co-authors
The 3 scholars most cited alongside Phillip E. C. Compeau, 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 | 2011 | 357 | |
| 2 | Bioinformatics Algorithms: An Active Learning Approach | 2014 | 26 |
| 3 | 2011 | 23 | |
| 4 | 2013 | 19 | |
| 5 | 2015 | 10 | |
| 6 | 2019 | 8 | |
| 7 | 2010 | 2 |
About Phillip E. C. Compeau
Phillip E. C. Compeau is a scholar working on Molecular Biology, Genetics, Artificial Intelligence, Plant Science and Media Technology, having authored 7 papers that have together received 445 indexed citations. Recurring topics across this work include Genome Rearrangement Algorithms (3 papers), Genetics, Bioinformatics, and Biomedical Research (2 papers), Chromosomal and Genetic Variations (2 papers), Genomics and Phylogenetic Studies (2 papers), DNA and Biological Computing (2 papers), Experimental Learning in Engineering (1 paper), Machine Learning in Bioinformatics (1 paper) and Online Learning and Analytics (1 paper). The work is most often cited by research in Molecular Biology (325 citations), Artificial Intelligence (96 citations), Plant Science (93 citations), Genetics (70 citations) and Computational Theory and Mathematics (32 citations). Phillip E. C. Compeau has collaborated with scholars based in United States. Frequent co-authors include Pavel A. Pevzner, Glenn Tesler and Laurie J. Heyer. Their work appears in journals such as Discrete Applied Mathematics, Algorithms for Molecular Biology, PLoS Computational Biology, Nature Biotechnology and Communications of the ACM.
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.