Daniel J. Fazakerley
- Physiology top 2%
- Adipose Tissue and Metabolism 30
- Molecular Biology top 5%
- Metabolism, Diabetes, and Cancer 32
- Mitochondrial Function and Pathology 9
- PI3K/AKT/mTOR signaling in cancer 6
- Cell Biology top 2%
- Cancer Research top 5%
- Cancer, Hypoxia, and Metabolism 6
- Aging top 5%
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- Pancreatic function and diabetes 13
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- Adipokines, Inflammation, and Metabolic Diseases 8
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- Advanced Proteomics Techniques and Applications 6
- Co-authors
- David E. JamesJacqueline StöckliJames R. KrycerPengyi YangSean J. HumphreyJames G. BurchfieldGuang YangBenjamin L. Parker
- Journals
- Journal of Biological Chemistry (10 papers)Nature Communications (1 paper)The Journal of Cell Biology (1 paper)
- Partner nations
- AustraliaUnited KingdomUnited States
In The Last Decade
Daniel J. Fazakerley
64 papers receiving 3.3k citations
Peers
Comparison fields: 5 of 121
- Physiology 1.2k
- Molecular Biology 2.2k
- Cell Biology 481
- Cancer Research 438
- Aging 47
Countries citing papers authored by Daniel J. Fazakerley
This map shows the geographic impact of Daniel J. Fazakerley'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 Daniel J. Fazakerley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel J. Fazakerley more than expected).
Fields of papers citing papers by Daniel J. Fazakerley
This network shows the impact of papers produced by Daniel J. Fazakerley. 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 Daniel J. Fazakerley. The network helps show where Daniel J. Fazakerley may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Daniel J. Fazakerley, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 3 | |
| 4 | 2024 | 5 | |
| 5 | 2023 | 17 | |
| 6 | 2022 | 11 | |
| 7 | 2022 | 36 | |
| 8 | 2021 | 13 | |
| 9 | 2019 | 19 | |
| 10 | 2019 | 17 | |
| 11 | 2018 | 11 | |
| 12 | 2017 | 15 | |
| 13 | 2017 | 329 | |
| 14 | 2016 | 11 | |
| 15 | Unravelling signal coordination from large scale phosphorylation kinetic data | 2016 | 1 |
| 16 | 2015 | 46 | |
| 17 | 2015 | 320 | |
| 18 | 2015 | 8 | |
| 19 | 2015 | 7 | |
| 20 | 2014 | 17 |
About Daniel J. Fazakerley
Daniel J. Fazakerley is a scholar working on Physiology, Molecular Biology and Aging, having authored 67 papers that have together received 3.3k indexed citations. Recurring topics across this work include Metabolism, Diabetes, and Cancer (32 papers), Adipose Tissue and Metabolism (30 papers), Pancreatic function and diabetes (13 papers), Mitochondrial Function and Pathology (9 papers), Adipokines, Inflammation, and Metabolic Diseases (8 papers), PI3K/AKT/mTOR signaling in cancer (6 papers), Advanced Proteomics Techniques and Applications (6 papers) and Cancer, Hypoxia, and Metabolism (6 papers). The work is most often cited by research in Physiology (1.2k citations), Molecular Biology (2.2k citations) and Cell Biology (481 citations). Daniel J. Fazakerley has collaborated with scholars based in Australia, United Kingdom and United States. Frequent co-authors include David E. James, Jacqueline Stöckli, James R. Krycer, Pengyi Yang, Sean J. Humphrey, James G. Burchfield, Guang Yang, Benjamin L. Parker, Jean Yang and Kristen C. Thomas. Their work appears in journals such as Journal of Biological Chemistry, Nature Communications and The Journal of Cell Biology.
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.