Jonathan M. Graff
- Aging top 0.5%
- Genetics, Aging, and Longevity in Model Organisms 8
- Physiology top 1%
- Adipose Tissue and Metabolism 15
- Molecular Biology top 1%
- TGF-β signaling in diseases 12
- Protein Kinase Regulation and GTPase Signaling 9
- Developmental Biology and Gene Regulation 7
- Cell Biology top 1%
- Genetics top 2%
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- Adipokines, Inflammation, and Metabolic Diseases 11
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- Enzyme Structure and Function 6
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- Neurobiology and Insect Physiology Research 5
- Co-authors
- Renée M. McKayPerry J. BlackshearDouglas A. MeltonDaniel C. BerryDaniel ZeveJae Myoung SuhWei TangYuwei Jiang
- Cited by
- AgingPhysiologyMolecular Biology
- Partner nations
- United StatesCanadaAustralia
In The Last Decade
Jonathan M. Graff
63 papers receiving 7.3k citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Aging 352
- Physiology 1.8k
- Molecular Biology 4.6k
- Cell Biology 990
- Genetics 430
Countries citing papers authored by Jonathan M. Graff
This map shows the geographic impact of Jonathan M. Graff'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 Jonathan M. Graff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan M. Graff more than expected).
Fields of papers citing papers by Jonathan M. Graff
This network shows the impact of papers produced by Jonathan M. Graff. 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 Jonathan M. Graff. The network helps show where Jonathan M. Graff may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jonathan M. Graff, 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 | 2018 | 25 | |
| 2 | 2017 | 45 | |
| 3 | 2016 | 146 | |
| 4 | 2016 | 11 | |
| 5 | 2014 | 45 | |
| 6 | 2013 | 146 | |
| 7 | 2012 | 25 | |
| 8 | 2011 | 75 | |
| 9 | 2010 | 36 | |
| 10 | White Fat Progenitor Cells Reside in the Adipose Vasculaturebreakdown → | 2008 | 866 |
| 11 | 2006 | 222 | |
| 12 | 2005 | 10 | |
| 13 | 2001 | 97 | |
| 14 | 2001 | 10 | |
| 15 | 2001 | 41 | |
| 16 | 1997 | 186 | |
| 17 | 1997 | 16 | |
| 18 | 1996 | 388 | |
| 19 | 1989 | 16 | |
| 20 | 1989 | 60 |
About Jonathan M. Graff
Jonathan M. Graff is a scholar working on Aging, Molecular Biology, Physiology, Geriatrics and Gerontology and Rehabilitation, having authored 63 papers that have together received 7.4k indexed citations. Recurring topics across this work include Adipose Tissue and Metabolism (15 papers), TGF-β signaling in diseases (12 papers), Adipokines, Inflammation, and Metabolic Diseases (11 papers), Protein Kinase Regulation and GTPase Signaling (9 papers), Genetics, Aging, and Longevity in Model Organisms (8 papers), Developmental Biology and Gene Regulation (7 papers), Enzyme Structure and Function (6 papers) and Neurobiology and Insect Physiology Research (5 papers). The work is most often cited by research in Aging (352 citations), Physiology (1.8k citations), Molecular Biology (4.6k citations), Cell Biology (990 citations) and Genetics (430 citations). Jonathan M. Graff has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include Renée M. McKay, Perry J. Blackshear, Douglas A. Melton, Daniel C. Berry, Daniel Zeve, Jae Myoung Suh, Wei Tang, Yuwei Jiang, Deborah J. Stumpo and Michael Kyba. Their work appears in journals such as Journal of Biological Chemistry, Cell Metabolism, Development, Nature Communications and 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.