Michael Pargett
Impact in
- Biophysics top 5%
- Cell Image Analysis Techniques
- Aging top 10%
Papers in
-
- Gene Regulatory Network Analysis 10
- Melanoma and MAPK Pathways 5
- Receptor Mechanisms and Signaling 4
- Metabolism, Diabetes, and Cancer 3
- Developmental Biology and Gene Regulation 3
- PI3K/AKT/mTOR signaling in cancer 3
-
- Cell Image Analysis Techniques 5
- Co-authors
- John G. Albeck (18 shared papers)David M. Umulis (5 shared papers)Taryn E. Gillies (5 shared papers)Hilary L. Ashe (2 shared papers)Robin E. Harris (2 shared papers)Catherine Sutcliffe (1 shared paper)Carolyn Teragawa (5 shared papers)Alexander E. Davies (3 shared papers)
- Journals
- eLife (2 papers)Cell Systems (2 papers)Biochemical Journal (2 papers)Nature Communications (1 paper)Developmental Cell (1 paper)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Michael Pargett
27 papers receiving 683 citations
Peers
Comparison fields: 5 of 86
- Biophysics 85
- Aging 19
- Molecular Biology 475
- Emergency Medicine 55
- Cell Biology 104
Countries citing papers authored by Michael Pargett
This map shows the geographic impact of Michael Pargett'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 Michael Pargett with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Pargett more than expected).
Fields of papers citing papers by Michael Pargett
This network shows the impact of papers produced by Michael Pargett. 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 Michael Pargett. The network helps show where Michael Pargett may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael Pargett, 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 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 127 | |
| 2 | 2015 | 75 | |
| 3 | 2017 | 60 | |
| 4 | 2017 | 58 | |
| 5 | 2020 | 42 | |
| 6 | 2017 | 41 | |
| 7 | 2021 | 34 | |
| 8 | 2020 | 32 | |
| 9 | 2009 | 27 | |
| 10 | 2017 | 25 | |
| 11 | 2023 | 22 | |
| 12 | 2023 | 22 | |
| 13 | 2013 | 19 | |
| 14 | 2014 | 19 | |
| 15 | 2008 | 17 | |
| 16 | 2008 | 14 | |
| 17 | 2018 | 10 | |
| 18 | 2012 | 9 | |
| 19 | 2021 | 8 | |
| 20 | 2013 | 6 |
About Michael Pargett
Michael Pargett is a scholar working on Molecular Biology, Biophysics, Surgery, Oncology and Emergency Medicine, having authored 28 papers that have together received 687 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (10 papers), Cell Image Analysis Techniques (5 papers), Melanoma and MAPK Pathways (5 papers), Receptor Mechanisms and Signaling (4 papers), Metabolism, Diabetes, and Cancer (3 papers), Cardiac Arrest and Resuscitation (3 papers), Developmental Biology and Gene Regulation (3 papers) and PI3K/AKT/mTOR signaling in cancer (3 papers). The work is most often cited by research in Biophysics (85 citations), Aging (19 citations), Molecular Biology (475 citations), Emergency Medicine (55 citations) and Cell Biology (104 citations). Michael Pargett has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include John G. Albeck, David M. Umulis, Taryn E. Gillies, Hilary L. Ashe, Robin E. Harris, Catherine Sutcliffe, Carolyn Teragawa, Alexander E. Davies, Ann E. Rundell and L. A. Geddes. Their work appears in journals such as eLife, Cell Systems, Biochemical Journal, Nature Communications and Developmental Cell.
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.