Nathan S. Doyle

719 citations
9 papers · 558 indexed · h-index 6

Impact in

Papers in

Nathan S. Doyle

9 papers receiving 547 citations

Peers

Nathan S. Doyle
Comparison fields: 5 of 65
  • Endocrinology, Diabetes and Metabolism 399
  • Genetics 110
  • Cancer Research 59
  • Endocrine and Autonomic Systems 24
  • Oncology 68
Replace Takahiro Tsushima with:
Takahiro Tsushima United States
Asako Miyazaki Japan
Pik To Cheung Hong Kong
M. Cubbage United States
Lucila Sackmann‐Sala United States
A. Gessi Italy
Anna S. Wilhelmson Sweden
M C Postel-Vinay France
Yuichi Fujinaka Japan
Silke Rosinger Germany
Nathan S. Doyle relative to Takahiro Tsushima United States Takahiro Tsushima's profile →
Citations per field
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Takahiro Tsushima · 1×
Citations per year

Countries citing papers authored by Nathan S. Doyle

Since Specialization
Citations

This map shows the geographic impact of Nathan S. Doyle'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 Nathan S. Doyle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan S. Doyle more than expected).

Fields of papers citing papers by Nathan S. Doyle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathan S. Doyle. 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 Nathan S. Doyle. The network helps show where Nathan S. Doyle may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Nathan S. Doyle, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Nathan S. Doyle Line = papers co-authored together Nathan S. Doyle links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 20235
2 20212
3 202024
4 20202
5 20079
6 2003181
7 2001115
8 20001
9 2000219

About Nathan S. Doyle

Nathan S. Doyle is a scholar working on Endocrinology, Diabetes and Metabolism, Neurology, Neurology, Radiology, Nuclear Medicine and Imaging and Oncology, having authored 9 papers that have together received 558 indexed citations. Recurring topics across this work include Growth Hormone and Insulin-like Growth Factors (5 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Lung Cancer Diagnosis and Treatment (2 papers), Metabolism, Diabetes, and Cancer (2 papers), Pancreatic function and diabetes (2 papers), Cerebral Venous Sinus Thrombosis (2 papers), Advanced X-ray and CT Imaging (2 papers) and Cytokine Signaling Pathways and Interactions (2 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (399 citations), Genetics (110 citations), Cancer Research (59 citations), Endocrine and Autonomic Systems (24 citations) and Oncology (68 citations). Nathan S. Doyle has collaborated with scholars based in Australia, United States and United Kingdom. Frequent co-authors include Ken K. Y. Ho, Mercedes Ballesteros, Michael J. Waters, Kin‐Chuen Leung, Richard Ross, K C Leung, Gary M. Leong, Klara Sjögren, C. K. W. Watts and Tsu‐Hui Low. Their work appears in journals such as Medical Physics, Endocrinology, The Journal of Clinical Endocrinology & Metabolism, Clinical Neuroradiology and Proceedings of the National Academy of Sciences.

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.

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