Winona C. Barker
- Molecular Biology top 2%
- Genomics and Phylogenetic Studies 20
- Machine Learning in Bioinformatics 12
- Glycosylation and Glycoproteins Research 9
- RNA and protein synthesis mechanisms 8
- Biomedical Text Mining and Ontologies 7
- Bioinformatics and Genomic Networks 5
- Virology top 5%
- Genetics top 5%
- Cell Biology top 5%
- Immunology top 10%
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- Advanced Proteomics Techniques and Applications 5
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- Monoclonal and Polyclonal Antibodies Research 4
- Co-authors
- Lois T. HuntM. O. DayhoffCathy WuDavid G. GeorgeHongzhan HuangLai-Su YehD. G. GeorgeA. N. NIKOL'SKAYA
- Cited by
- Molecular BiologyVirologyGenetics
- Journals
- Science (2 papers)Proceedings of the National Academy of Sciences (2 papers)Nucleic Acids Research (10 papers)
- Partner nations
- United StatesJapanGermany
In The Last Decade
Winona C. Barker
55 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Molecular Biology 2.7k
- Virology 108
- Genetics 514
- Cell Biology 263
- Immunology 309
Countries citing papers authored by Winona C. Barker
This map shows the geographic impact of Winona C. Barker'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 Winona C. Barker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Winona C. Barker more than expected).
Fields of papers citing papers by Winona C. Barker
This network shows the impact of papers produced by Winona C. Barker. 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 Winona C. Barker. The network helps show where Winona C. Barker may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Winona C. Barker, 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 | 2010 | 88 | |
| 2 | 2009 | 13 | |
| 3 | 2004 | 77 | |
| 4 | 2003 | 87 | |
| 5 | 2000 | 139 | |
| 6 | 2000 | 29 | |
| 7 | 1999 | 123 | |
| 8 | 1999 | 20 | |
| 9 | 1998 | 60 | |
| 10 | 1993 | 55 | |
| 11 | 1992 | 17 | |
| 12 | 1991 | 32 | |
| 13 | 1990 | 1 | |
| 14 | 1988 | 15 | |
| 15 | 1988 | 2 | |
| 16 | 1988 | 12 | |
| 17 | 1987 | 48 | |
| 18 | 1984 | 24 | |
| 19 | [47] Establishing homologies in protein sequencesbreakdown → | 1983 | 1050 |
| 20 | 1979 | 10 |
About Winona C. Barker
Winona C. Barker is a scholar working on Molecular Biology, Biotechnology and Cell Biology, having authored 55 papers that have together received 3.7k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (20 papers), Machine Learning in Bioinformatics (12 papers), Glycosylation and Glycoproteins Research (9 papers), RNA and protein synthesis mechanisms (8 papers), Biomedical Text Mining and Ontologies (7 papers), Advanced Proteomics Techniques and Applications (5 papers), Bioinformatics and Genomic Networks (5 papers) and Monoclonal and Polyclonal Antibodies Research (4 papers). The work is most often cited by research in Molecular Biology (2.7k citations), Virology (108 citations) and Genetics (514 citations). Winona C. Barker has collaborated with scholars based in United States, Japan and Germany. Frequent co-authors include Lois T. Hunt, M. O. Dayhoff, Cathy Wu, David G. George, Hongzhan Huang, Lai-Su Yeh, D. G. George, A. N. NIKOL'SKAYA, Cecilia N. Arighi and John S. Garavelli. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.
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.