Keisha Hearn
Impact in
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- Heat shock proteins research
- Melanoma and MAPK Pathways
- Protein Degradation and Inhibitors
- Ubiquitin and proteasome pathways
- ATP Synthase and ATPases Research
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- Computational Drug Discovery Methods
Papers in
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- ATP Synthase and ATPases Research 2
- Ubiquitin and proteasome pathways 1
- Biochemical and Molecular Research 1
- Melanoma and MAPK Pathways 1
- Cell death mechanisms and regulation 1
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- Lung Cancer Treatments and Mutations 2
- Co-authors
- Tomoko Smyth (3 shared papers)Neil T. Thompson (3 shared papers)Nicola G. Wallis (3 shared papers)John F. Lyons (2 shared papers)Kim H.T. Paraiso (1 shared paper)Keiran S.M. Smalley (1 shared paper)Joanne M. Munck (1 shared paper)Vernon K. Sondak (1 shared paper)
- Journals
- Journal of Medicinal Chemistry (1 paper)Cancer Research (1 paper)Molecular Cancer Therapeutics (1 paper)British Journal of Cancer (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
Keisha Hearn
4 papers receiving 143 citations
Peers
Comparison fields: 5 of 35
- Molecular Biology 117
- Computational Theory and Mathematics 24
- Oncology 30
- Cell Biology 16
- Toxicology 3
Countries citing papers authored by Keisha Hearn
This map shows the geographic impact of Keisha Hearn'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 Keisha Hearn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keisha Hearn more than expected).
Fields of papers citing papers by Keisha Hearn
This network shows the impact of papers produced by Keisha Hearn. 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 Keisha Hearn. The network helps show where Keisha Hearn may publish in the future.
Co-authors
The 25 scholars most cited alongside Keisha Hearn, 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 | 2014 | 66 | |
| 2 | 2017 | 57 | |
| 3 | 2016 | 23 | |
| 4 | 2015 | 2 |
About Keisha Hearn
Keisha Hearn is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine, Computational Theory and Mathematics, Cell Biology and Infectious Diseases, having authored 4 papers that have together received 148 indexed citations. Recurring topics across this work include Lung Cancer Treatments and Mutations (2 papers), ATP Synthase and ATPases Research (2 papers), Ubiquitin and proteasome pathways (1 paper), Computational Drug Discovery Methods (1 paper), Biochemical and Molecular Research (1 paper), Melanoma and MAPK Pathways (1 paper), Cell death mechanisms and regulation (1 paper) and Cellular Mechanics and Interactions (1 paper). The work is most often cited by research in Molecular Biology (117 citations), Computational Theory and Mathematics (24 citations), Oncology (30 citations), Cell Biology (16 citations) and Toxicology (3 citations). Keisha Hearn has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Tomoko Smyth, Neil T. Thompson, Nicola G. Wallis, John F. Lyons, Kim H.T. Paraiso, Keiran S.M. Smalley, Joanne M. Munck, Vernon K. Sondak, H. Eirik Haarberg and Mohammad Azab. Their work appears in journals such as Journal of Medicinal Chemistry, Cancer Research, Molecular Cancer Therapeutics and British Journal of Cancer.
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.