George Au‐Yeung

3.3k total citations
43 papers, 707 citations indexed

About

George Au‐Yeung is a scholar working on Oncology, Molecular Biology and Reproductive Medicine. According to data from OpenAlex, George Au‐Yeung has authored 43 papers receiving a total of 707 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Oncology, 18 papers in Molecular Biology and 11 papers in Reproductive Medicine. Recurrent topics in George Au‐Yeung's work include Cancer Immunotherapy and Biomarkers (12 papers), Ovarian cancer diagnosis and treatment (11 papers) and Melanoma and MAPK Pathways (8 papers). George Au‐Yeung is often cited by papers focused on Cancer Immunotherapy and Biomarkers (12 papers), Ovarian cancer diagnosis and treatment (11 papers) and Melanoma and MAPK Pathways (8 papers). George Au‐Yeung collaborates with scholars based in Australia, United States and Switzerland. George Au‐Yeung's co-authors include David D.L. Bowtell, Dariush Etemadmoghadam, Linda Mileshkin, William C. Hahn, Barbara A. Weir, Joshy George, Thomas J. Mitchell, Kaylene J. Simpson, Danny Rischin and Alan D. D’Andrea and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and Journal of Clinical Oncology.

In The Last Decade

George Au‐Yeung

37 papers receiving 702 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
George Au‐Yeung Australia 15 463 297 168 119 103 43 707
Caroline O. Michie United Kingdom 10 216 0.5× 207 0.7× 182 1.1× 50 0.4× 154 1.5× 20 562
Cristina Mirantes Spain 13 168 0.4× 279 0.9× 62 0.4× 63 0.5× 112 1.1× 17 595
Koji Yamanoi Japan 12 464 1.0× 290 1.0× 164 1.0× 134 1.1× 228 2.2× 72 946
Satoshi Tsunetoh Japan 15 243 0.5× 280 0.9× 235 1.4× 67 0.6× 159 1.5× 32 715
Eva Obermayr Austria 15 508 1.1× 327 1.1× 183 1.1× 144 1.2× 481 4.7× 44 953
Catherine J. Kennedy Australia 13 194 0.4× 234 0.8× 85 0.5× 176 1.5× 119 1.2× 25 560
Monica Mannelqvist Norway 12 183 0.4× 266 0.9× 77 0.5× 83 0.7× 164 1.6× 15 544
Sandeep Kumar Parvathareddy Saudi Arabia 15 213 0.5× 280 0.9× 53 0.3× 84 0.7× 139 1.3× 57 626
Lisa Meyer United States 8 401 0.9× 393 1.3× 110 0.7× 59 0.5× 157 1.5× 10 733
Takeshi Hirasawa Japan 15 118 0.3× 187 0.6× 191 1.1× 94 0.8× 143 1.4× 63 581

Countries citing papers authored by George Au‐Yeung

Since Specialization
Citations

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

Fields of papers citing papers by George Au‐Yeung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by George Au‐Yeung. 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 George Au‐Yeung. The network helps show where George Au‐Yeung may publish in the future.

Co-authorship network of co-authors of George Au‐Yeung

This figure shows the co-authorship network connecting the top 25 collaborators of George Au‐Yeung. A scholar is included among the top collaborators of George Au‐Yeung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with George Au‐Yeung. George Au‐Yeung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hong, Martin, Jun Hee Hong, George Au‐Yeung, et al.. (2025). Application of Generative Artificial Intelligence for Physician and Patient Oncology Letters—AI-OncLetters. JCO Clinical Cancer Informatics. 9(9). e2400323–e2400323.
2.
Sullivan, Ryan J., J.C. Hassel, Teresa Amaral, et al.. (2025). 954P A randomized phase II study of autogene cevumeran plus pembrolizumab (pembro) versus pembro in 1L advanced melanoma (IMcode001). Annals of Oncology. 36. S585–S585.
3.
Long, Georgina V., Matteo S. Carlino, George Au‐Yeung, et al.. (2024). Neoadjuvant pembrolizumab, dabrafenib and trametinib in BRAFV600-mutant resectable melanoma: the randomized phase 2 NeoTrio trial. Nature Medicine. 30(9). 2540–2548. 15 indexed citations
4.
Mian, Firoz, et al.. (2024). Cancer and treatment specific incidence rates of immune-related adverse events induced by immune checkpoint inhibitors: a systematic review. British Journal of Cancer. 132(1). 51–57. 23 indexed citations
5.
Zhou, Li, Edward Hsiao, Serigne Lo, et al.. (2024). FDG-PET associations with pathological response and survival with neoadjuvant immunotherapy for melanoma.. Journal of Clinical Oncology. 42(16_suppl). 9523–9523.
6.
Au‐Yeung, George, et al.. (2024). Innovations in Rare Gynecologic Cancer: Melanoma, Neuroendocrine, and Low-Grade Serous Ovarian. American Society of Clinical Oncology Educational Book. 44(3). e431818–e431818.
7.
Bressel, Mathias, Yi-An Ko, Anne Hamilton, et al.. (2024). Beacon: A phase II study of bevacizumab, atezolizumab, and cobimetinib in patients with recurrent, platinum resistant, high grade serous ovarian cancer.. Journal of Clinical Oncology. 42(16_suppl). 5565–5565. 1 indexed citations
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Mian, Firoz, et al.. (2024). Analysis of risk factors for immune-related adverse events induced by immune checkpoint inhibitor treatment in cancer: A comprehensive systematic review. Critical Reviews in Oncology/Hematology. 207. 104601–104601. 2 indexed citations
11.
Meric‐Bernstam, Funda, Mi‐Yeon Song, Shannon N. Westin, et al.. (2023). 819TiP FONTANA: A phase I/IIa study of AZD5335 as monotherapy and in combination with anti-cancer agents in patients with solid tumours. Annals of Oncology. 34. S541–S541. 4 indexed citations
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Azar, Walid J., Elizabeth L. Christie, Thomas J. Mitchell, et al.. (2020). Noncanonical IL6 Signaling-Mediated Activation of YAP Regulates Cell Migration and Invasion in Ovarian Clear Cell Cancer. Cancer Research. 80(22). 4960–4971. 14 indexed citations
14.
Weppler, Alison M., Andrew Pattison, Prachi Bhave, et al.. (2020). Clinical, FDG-PET and molecular markers of immune checkpoint inhibitor response in patients with metastatic Merkel cell carcinoma. Journal for ImmunoTherapy of Cancer. 8(2). e000700–e000700. 8 indexed citations
15.
Hyatt, Amelia, Karla Gough, Andrew Murnane, et al.. (2020). i-Move, a personalised exercise intervention for patients with advanced melanoma receiving immunotherapy: a randomised feasibility trial protocol. BMJ Open. 10(2). e036059–e036059. 11 indexed citations
16.
Beesley, Vanessa L., George Au‐Yeung, Christina M. Nagle, et al.. (2019). Does physical activity improve chemotherapy completion in women receiving chemotherapy for ovarian cancer. Asia-Pacific Journal of Clinical Oncology. 15. 82–83.
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Au‐Yeung, George, Walid J. Azar, Thomas J. Mitchell, et al.. (2016). Selective Targeting of Cyclin E1-Amplified High-Grade Serous Ovarian Cancer by Cyclin-Dependent Kinase 2 and AKT Inhibition. Clinical Cancer Research. 23(7). 1862–1874. 100 indexed citations
19.
Etemadmoghadam, Dariush, George Au‐Yeung, Meaghan Wall, et al.. (2013). Resistance to CDK2 Inhibitors Is Associated with Selection of Polyploid Cells in CCNE1 -Amplified Ovarian Cancer. Clinical Cancer Research. 19(21). 5960–5971. 90 indexed citations
20.
Etemadmoghadam, Dariush, Barbara A. Weir, George Au‐Yeung, et al.. (2013). Synthetic lethality between CCNE1 amplification and loss of BRCA1. Proceedings of the National Academy of Sciences. 110(48). 19489–19494. 157 indexed citations

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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