John M. Hatcher
Impact in
- Oncology top 10%
- Peptidase Inhibition and Analysis
- Molecular Biology top 10%
- Protein Degradation and Inhibitors
- Ubiquitin and proteasome pathways
- Chemical Synthesis and Analysis
Papers in
- Co-authors
- Nathanael S. Gray (24 shared papers)Tinghu Zhang (4 shared papers)Don M. Coltart (2 shared papers)Hwan Geun Choi (5 shared papers)Mingxing Teng (2 shared papers)Dario R. Alessi (4 shared papers)Milka Kostić (1 shared paper)Jinwei Zhang (2 shared papers)
- Journals
- ACS Medicinal Chemistry Letters (7 papers)Bioorganic & Medicinal Chemistry Letters (3 papers)Journal of Medicinal Chemistry (3 papers)British Journal of Haematology (2 papers)Cell chemical biology (2 papers)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
John M. Hatcher
33 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 88
- Oncology 331
- Molecular Biology 865
- Neurology 175
- Hematology 115
- Organic Chemistry 283
Countries citing papers authored by John M. Hatcher
This map shows the geographic impact of John M. Hatcher'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 John M. Hatcher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John M. Hatcher more than expected).
Fields of papers citing papers by John M. Hatcher
This network shows the impact of papers produced by John M. Hatcher. 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 John M. Hatcher. The network helps show where John M. Hatcher may publish in the future.
Co-authors
The 25 scholars most cited alongside John M. Hatcher, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 276 | |
| 2 | 2019 | 149 | |
| 3 | 2012 | 117 | |
| 4 | 2018 | 109 | |
| 5 | 2010 | 72 | |
| 6 | 2018 | 71 | |
| 7 | 2020 | 68 | |
| 8 | 2017 | 60 | |
| 9 | 2015 | 51 | |
| 10 | 2018 | 51 | |
| 11 | 2015 | 48 | |
| 12 | 2015 | 39 | |
| 13 | 2019 | 35 | |
| 14 | 2023 | 35 | |
| 15 | 2003 | 33 | |
| 16 | 2017 | 31 | |
| 17 | 2011 | 21 | |
| 18 | 2016 | 19 | |
| 19 | 2019 | 18 | |
| 20 | 2020 | 18 |
About John M. Hatcher
John M. Hatcher is a scholar working on Genetics, Oncology, Molecular Biology, Cancer Research and Neurology, having authored 35 papers that have together received 1.4k indexed citations. Recurring topics across this work include Protein Degradation and Inhibitors (9 papers), Ubiquitin and proteasome pathways (8 papers), Chronic Lymphocytic Leukemia Research (7 papers), NF-κB Signaling Pathways (5 papers), Parkinson's Disease Mechanisms and Treatments (3 papers), Plant Gene Expression Analysis (3 papers), Histone Deacetylase Inhibitors Research (3 papers) and Peptidase Inhibition and Analysis (3 papers). The work is most often cited by research in Oncology (331 citations), Molecular Biology (865 citations), Neurology (175 citations), Hematology (115 citations) and Organic Chemistry (283 citations). John M. Hatcher has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Nathanael S. Gray, Tinghu Zhang, Don M. Coltart, Hwan Geun Choi, Mingxing Teng, Dario R. Alessi, Milka Kostić, Jinwei Zhang, Nicholas P. Kwiatkowski and Taebo Sim. Their work appears in journals such as ACS Medicinal Chemistry Letters, Bioorganic & Medicinal Chemistry Letters, Journal of Medicinal Chemistry, British Journal of Haematology and Cell chemical 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.