Danielle L. Brown
Impact in
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- Bone health and osteoporosis research
- Biophysics top 10%
- Cell Image Analysis Techniques
Papers in
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- Immunotoxicology and immune responses 4
- Co-authors
- Amiya P. Sinha Hikim (2 shared papers)Ekaterina Kovacheva (2 shared papers)Indrani Sinha‐Hikim (2 shared papers)Michael S. Ominsky (3 shared papers)Rogely Boyce (3 shared papers)Ian Pyrah (3 shared papers)Melissa Braga (1 shared paper)Néstor F. González-Cadavid (1 shared paper)
- Journals
- Toxicologic Pathology (6 papers)Bone (2 papers)Veterinary Pathology (1 paper)PLoS ONE (1 paper)Toxicology Letters (1 paper)
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Danielle L. Brown
16 papers receiving 509 citations
Peers
Comparison fields: 5 of 102
- Orthopedics and Sports Medicine 104
- Biophysics 35
- Oncology 109
- Molecular Biology 269
- Health Informatics 5
Countries citing papers authored by Danielle L. Brown
This map shows the geographic impact of Danielle L. Brown'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 Danielle L. Brown with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle L. Brown more than expected).
Fields of papers citing papers by Danielle L. Brown
This network shows the impact of papers produced by Danielle L. Brown. 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 Danielle L. Brown. The network helps show where Danielle L. Brown may publish in the future.
Co-authors
The 25 scholars most cited alongside Danielle L. Brown, 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 | 2008 | 102 | |
| 2 | 2015 | 78 | |
| 3 | 2015 | 73 | |
| 4 | 2014 | 46 | |
| 5 | 2017 | 41 | |
| 6 | 2019 | 39 | |
| 7 | 2017 | 37 | |
| 8 | 2009 | 36 | |
| 9 | 2018 | 24 | |
| 10 | 2020 | 14 | |
| 11 | 2019 | 11 | |
| 12 | 2017 | 10 | |
| 13 | 2008 | 5 | |
| 14 | 2009 | 3 | |
| 15 | 2020 | 3 | |
| 16 | 2021 | 1 |
About Danielle L. Brown
Danielle L. Brown is a scholar working on Molecular Biology, Immunology, Applied Mathematics, Oncology and Artificial Intelligence, having authored 16 papers that have together received 523 indexed citations. Recurring topics across this work include Point processes and geometric inequalities (4 papers), Immunotoxicology and immune responses (4 papers), AI in cancer detection (3 papers), Cellular Mechanics and Interactions (2 papers), Cell Image Analysis Techniques (2 papers), Nerve injury and regeneration (2 papers), Alzheimer's disease research and treatments (1 paper) and Hepatitis B Virus Studies (1 paper). The work is most often cited by research in Orthopedics and Sports Medicine (104 citations), Biophysics (35 citations), Oncology (109 citations), Molecular Biology (269 citations) and Health Informatics (5 citations). Danielle L. Brown has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Amiya P. Sinha Hikim, Ekaterina Kovacheva, Indrani Sinha‐Hikim, Michael S. Ominsky, Rogely Boyce, Ian Pyrah, Melissa Braga, Néstor F. González-Cadavid, Mónica G. Ferrini and J. Ignacio Aguirre. Their work appears in journals such as Toxicologic Pathology, Bone, Veterinary Pathology, PLoS ONE and Toxicology Letters.
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.