Kent Shefchek
Impact in
Papers in
-
- Biomedical Text Mining and Ontologies 4
- Bioinformatics and Genomic Networks 2
- Genomics and Phylogenetic Studies 1
- Genetics 4
- Genomics and Rare Diseases 3
- Genomic variations and chromosomal abnormalities 2
- Co-authors
- Silke Ruppel (1 shared paper)Suvarna Nadendla (1 shared paper)Katja Witzel (1 shared paper)Melissa Haendel (6 shared papers)Chris Mungall (2 shared papers)Peter N. Robinson (4 shared papers)Tiffany J. Callahan (1 shared paper)Vida Ravanmehr (1 shared paper)
- Journals
- Journal of Bacteriology (2 papers)Human Mutation (1 paper)Pathogens and Disease (1 paper)Genetics in Medicine (1 paper)Journal of Medical Internet Research (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Kent Shefchek
9 papers receiving 159 citations
Peers
Comparison fields: 5 of 63
- Microbiology 3
- Small Animals 15
- Health Informatics 2
- Molecular Biology 81
- Artificial Intelligence 36
Countries citing papers authored by Kent Shefchek
This map shows the geographic impact of Kent Shefchek'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 Kent Shefchek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kent Shefchek more than expected).
Fields of papers citing papers by Kent Shefchek
This network shows the impact of papers produced by Kent Shefchek. 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 Kent Shefchek. The network helps show where Kent Shefchek may publish in the future.
Co-authors
The 25 scholars most cited alongside Kent Shefchek, 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 | 2020 | 54 | |
| 2 | 2012 | 46 | |
| 3 | 2015 | 19 | |
| 4 | 2012 | 15 | |
| 5 | 2013 | 14 | |
| 6 | SEPIO: A semantic model for the integration and analysis of scientific evidence | 2016 | 6 |
| 7 | 2022 | 3 | |
| 8 | 2021 | 3 | |
| 9 | 2019 | 3 |
About Kent Shefchek
Kent Shefchek is a scholar working on Molecular Biology, Genetics, Artificial Intelligence, Infectious Diseases and Surgery, having authored 9 papers that have together received 163 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (4 papers), Genomics and Rare Diseases (3 papers), Bioinformatics and Genomic Networks (2 papers), Genomic variations and chromosomal abnormalities (2 papers), Microbial Metabolites in Food Biotechnology (1 paper), Semantic Web and Ontologies (1 paper), Mycobacterium research and diagnosis (1 paper) and Genomics and Phylogenetic Studies (1 paper). The work is most often cited by research in Microbiology (3 citations), Small Animals (15 citations), Health Informatics (2 citations), Molecular Biology (81 citations) and Artificial Intelligence (36 citations). Kent Shefchek has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Silke Ruppel, Suvarna Nadendla, Katja Witzel, Melissa Haendel, Chris Mungall, Peter N. Robinson, Tiffany J. Callahan, Vida Ravanmehr, Hannah Blau and Luca Cappelletti. Their work appears in journals such as Journal of Bacteriology, Human Mutation, Pathogens and Disease, Genetics in Medicine and Journal of Medical Internet 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.