Danielle Miller
Impact in
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
- Infectious Diseases top 10%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
Papers in
-
- Biochemical and Molecular Research 3
- Machine Learning in Bioinformatics 2
- Genomics and Phylogenetic Studies 2
- Genetics 7
- Evolution and Genetic Dynamics 6
- Co-authors
- Walter Hu (2 shared papers)Kevin J Luebke (2 shared papers)Feng Shen (1 shared paper)Manohar Ratnam (1 shared paper)John F. Ross (1 shared paper)Adi Stern (8 shared papers)Robert H. White (5 shared papers)David Burstein (3 shared papers)
- Journals
- Biochemistry (5 papers)Nature Communications (3 papers)Nature Medicine (1 paper)Journal of Bacteriology (1 paper)Bioinformatics (1 paper)
- Partner nations
- United StatesIsraelBelgium
In The Last Decade
Danielle Miller
23 papers receiving 593 citations
Peers
Comparison fields: 5 of 109
- Modeling and Simulation 76
- Infectious Diseases 140
- Rheumatology 70
- Biomaterials 54
- Surfaces, Coatings and Films 24
Countries citing papers authored by Danielle Miller
This map shows the geographic impact of Danielle Miller'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 Miller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle Miller more than expected).
Fields of papers citing papers by Danielle Miller
This network shows the impact of papers produced by Danielle Miller. 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 Miller. The network helps show where Danielle Miller may publish in the future.
Co-authors
The 25 scholars most cited alongside Danielle Miller, 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 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1995 | 138 | |
| 2 | 2009 | 104 | |
| 3 | 2020 | 89 | |
| 4 | 2022 | 73 | |
| 5 | 2010 | 36 | |
| 6 | 2022 | 26 | |
| 7 | 2020 | 20 | |
| 8 | 2015 | 17 | |
| 9 | 2014 | 13 | |
| 10 | 2012 | 13 | |
| 11 | 2024 | 12 | |
| 12 | 2015 | 9 | |
| 13 | 2022 | 8 | |
| 14 | 2022 | 8 | |
| 15 | 2015 | 7 | |
| 16 | 2019 | 7 | |
| 17 | 2019 | 6 | |
| 18 | 2024 | 3 | |
| 19 | 2024 | 3 | |
| 20 | 2011 | 3 |
About Danielle Miller
Danielle Miller is a scholar working on Molecular Biology, Genetics, Infectious Diseases, Materials Chemistry and Biomedical Engineering, having authored 24 papers that have together received 599 indexed citations. Recurring topics across this work include Evolution and Genetic Dynamics (6 papers), SARS-CoV-2 and COVID-19 Research (3 papers), Biochemical and Molecular Research (3 papers), Enzyme Structure and Function (3 papers), Machine Learning in Bioinformatics (2 papers), Genomics and Phylogenetic Studies (2 papers), Plant Virus Research Studies (2 papers) and Disaster Management and Resilience (2 papers). The work is most often cited by research in Modeling and Simulation (76 citations), Infectious Diseases (140 citations), Rheumatology (70 citations), Biomaterials (54 citations) and Surfaces, Coatings and Films (24 citations). Danielle Miller has collaborated with scholars based in United States, Israel and Belgium. Frequent co-authors include Walter Hu, Kevin J Luebke, Feng Shen, Manohar Ratnam, John F. Ross, Adi Stern, Robert H. White, David Burstein, Huimin Xu and Sheri Harari. Their work appears in journals such as Biochemistry, Nature Communications, Nature Medicine, Journal of Bacteriology and Bioinformatics.
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.