Sam Michael
Impact in
- Infectious Diseases top 2%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- Viral Infections and Vectors
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- Computational Drug Discovery Methods
Papers in
-
- Computational Drug Discovery Methods 15
-
- Cell Image Analysis Techniques 7
- Co-authors
- Anton Simeonov (19 shared papers)Paul Shinn (17 shared papers)Christopher P. Austin (7 shared papers)Wei Zheng (12 shared papers)Marc Ferrer (11 shared papers)Ruili Huang (8 shared papers)Ajit Jadhav (5 shared papers)Menghang Xia (5 shared papers)
- Journals
- SLAS TECHNOLOGY (5 papers)ACS Chemical Biology (4 papers)SLAS DISCOVERY (4 papers)Nature Communications (4 papers)Scientific Reports (3 papers)
- Partner nations
- United StatesTaiwanChina
In The Last Decade
Sam Michael
76 papers receiving 4.4k citations
Sam Michael's Hit Papers
Peers
Comparison fields: 5 of 158
- Infectious Diseases 799
- Computational Theory and Mathematics 655
- Biophysics 145
- Molecular Biology 1.6k
- Genetics 226
Countries citing papers authored by Sam Michael
This map shows the geographic impact of Sam Michael'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 Sam Michael with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Michael more than expected).
Fields of papers citing papers by Sam Michael
This network shows the impact of papers produced by Sam Michael. 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 Sam Michael. The network helps show where Sam Michael may publish in the future.
Co-authors
The 25 scholars most cited alongside Sam Michael, 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 77 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Identification of small-molecule inhibitors of Zika virus infection and induced neural cell death via a drug repurposing screen Hit paper breakdown → | 2016 | 553 |
| 2 | The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment Hit paper breakdown → | 2020 | 398 |
| 3 | 2014 | 324 | |
| 4 | 2013 | 220 | |
| 5 | 2014 | 188 | |
| 6 | 2020 | 179 | |
| 7 | 2019 | 161 | |
| 8 | 2014 | 147 | |
| 9 | 2021 | 145 | |
| 10 | 2008 | 123 | |
| 11 | 2021 | 117 | |
| 12 | 2019 | 103 | |
| 13 | 2021 | 96 | |
| 14 | 2020 | 95 | |
| 15 | 2020 | 94 | |
| 16 | 2021 | 92 | |
| 17 | 2020 | 87 | |
| 18 | 2015 | 87 | |
| 19 | 2018 | 77 | |
| 20 | 2008 | 75 |
About Sam Michael
Sam Michael is a scholar working on Computational Theory and Mathematics, Biophysics, Molecular Biology, Infectious Diseases and Genetics, having authored 77 papers that have together received 4.5k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (15 papers), 3D Printing in Biomedical Research (11 papers), SARS-CoV-2 and COVID-19 Research (8 papers), Cell Image Analysis Techniques (7 papers), COVID-19 Clinical Research Studies (6 papers), Viral Infectious Diseases and Gene Expression in Insects (4 papers), Cancer Cells and Metastasis (4 papers) and CRISPR and Genetic Engineering (4 papers). The work is most often cited by research in Infectious Diseases (799 citations), Computational Theory and Mathematics (655 citations), Biophysics (145 citations), Molecular Biology (1.6k citations) and Genetics (226 citations). Sam Michael has collaborated with scholars based in United States, Taiwan and China. Frequent co-authors include Anton Simeonov, Paul Shinn, Christopher P. Austin, Wei Zheng, Marc Ferrer, Ruili Huang, Ajit Jadhav, Menghang Xia, Raymond R. Tice and Hui Guo. Their work appears in journals such as SLAS TECHNOLOGY, ACS Chemical Biology, SLAS DISCOVERY, Nature Communications and Scientific 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.