Nathan Johnson
Impact in
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- Computational Drug Discovery Methods
Papers in
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- RNA modifications and cancer 3
- Gene expression and cancer classification 2
- Cancer-related gene regulation 2
- Epigenetics and DNA Methylation 2
- Bioinformatics and Genomic Networks 2
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- Radiopharmaceutical Chemistry and Applications 3
- Monoclonal and Polyclonal Antibodies Research 2
- Co-authors
- Dmitry Korkin (6 shared papers)Artem Sokolov (2 shared papers)Andi Dhroso (3 shared papers)Nienke Moret (1 shared paper)Bradley T. Hyman (1 shared paper)Clemens B. Hug (1 shared paper)George Zhou (1 shared paper)Mark W. Albers (1 shared paper)
- Journals
- Scientific Reports (2 papers)RNA (2 papers)Methods (1 paper)Mammalian Genome (1 paper)Cell Reports (1 paper)
- Partner nations
- United StatesCanadaSaudi Arabia
In The Last Decade
Nathan Johnson
15 papers receiving 472 citations
Nathan Johnson's Hit Papers
Peers
Comparison fields: 5 of 81
- Health Informatics 7
- Computational Theory and Mathematics 83
- Molecular Biology 204
- Biophysics 17
- Plant Science 102
Countries citing papers authored by Nathan Johnson
This map shows the geographic impact of Nathan Johnson'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 Nathan Johnson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Johnson more than expected).
Fields of papers citing papers by Nathan Johnson
This network shows the impact of papers produced by Nathan Johnson. 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 Nathan Johnson. The network helps show where Nathan Johnson may publish in the future.
Co-authors
The 25 scholars most cited alongside Nathan Johnson, 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 | Machine learning identifies candidates for drug repurposing in Alzheimer’s disease Hit paper breakdown → | 2021 | 208 |
| 2 | 2019 | 65 | |
| 3 | 2015 | 50 | |
| 4 | 2018 | 35 | |
| 5 | 2018 | 33 | |
| 6 | 2015 | 20 | |
| 7 | 2020 | 16 | |
| 8 | 2017 | 16 | |
| 9 | 2011 | 8 | |
| 10 | 2024 | 7 | |
| 11 | 2021 | 7 | |
| 12 | 2021 | 6 | |
| 13 | 2019 | 3 | |
| 14 | 2024 | 1 | |
| 15 | Therapeutic Efficacy of Pb-214-labeled Trastuzumab in a Preclinical Model of Ovarian Cancer | 2021 | 1 |
About Nathan Johnson
Nathan Johnson is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging, Pharmacology, Oncology and Genetics, having authored 15 papers that have together received 476 indexed citations. Recurring topics across this work include Radiopharmaceutical Chemistry and Applications (3 papers), RNA modifications and cancer (3 papers), Gene expression and cancer classification (2 papers), Cancer-related gene regulation (2 papers), Monoclonal and Polyclonal Antibodies Research (2 papers), HER2/EGFR in Cancer Research (2 papers), Epigenetics and DNA Methylation (2 papers) and Bioinformatics and Genomic Networks (2 papers). The work is most often cited by research in Health Informatics (7 citations), Computational Theory and Mathematics (83 citations), Molecular Biology (204 citations), Biophysics (17 citations) and Plant Science (102 citations). Nathan Johnson has collaborated with scholars based in United States, Canada and Saudi Arabia. Frequent co-authors include Dmitry Korkin, Artem Sokolov, Andi Dhroso, Nienke Moret, Bradley T. Hyman, Clemens B. Hug, George Zhou, Mark W. Albers, Sarah A. Boswell and Petar V. Todorov. Their work appears in journals such as Scientific Reports, RNA, Methods, Mammalian Genome and Cell Reports.
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.