Gary M. Tse

19.5k total citations · 4 hit papers
324 papers, 11.4k citations indexed

About

Gary M. Tse is a scholar working on Pathology and Forensic Medicine, Oncology and Cancer Research. According to data from OpenAlex, Gary M. Tse has authored 324 papers receiving a total of 11.4k indexed citations (citations by other indexed papers that have themselves been cited), including 133 papers in Pathology and Forensic Medicine, 115 papers in Oncology and 90 papers in Cancer Research. Recurrent topics in Gary M. Tse's work include Breast Lesions and Carcinomas (122 papers), Cancer and Skin Lesions (83 papers) and Breast Cancer Treatment Studies (75 papers). Gary M. Tse is often cited by papers focused on Breast Lesions and Carcinomas (122 papers), Cancer and Skin Lesions (83 papers) and Breast Cancer Treatment Studies (75 papers). Gary M. Tse collaborates with scholars based in Hong Kong, China and Singapore. Gary M. Tse's co-authors include Julia Y. Tsang, Puay Hoon Tan, Ann D. King, Anil T. Ahuja, Bonita K.B. Law, Philip C.W. Lui, Yun‐Bi Ni, Thomas Choudary Putti, Alexander C. Vlantis and Wei Yang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer and Oncogene.

In The Last Decade

Gary M. Tse

312 papers receiving 11.1k citations

Hit Papers

Breast cancer prognostic classification in the molecular ... 2010 2026 2015 2020 2010 2010 2019 2024 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gary M. Tse Hong Kong 57 3.6k 3.5k 3.4k 2.4k 2.1k 324 11.4k
Fernando Schmitt Portugal 60 5.3k 1.5× 2.1k 0.6× 4.4k 1.3× 5.5k 2.2× 930 0.4× 315 13.0k
J. Michael Dixon United Kingdom 54 4.1k 1.1× 3.1k 0.9× 5.3k 1.5× 2.5k 1.0× 694 0.3× 270 10.8k
Barbara A. Pockaj United States 53 4.1k 1.1× 1.6k 0.4× 2.4k 0.7× 1.4k 0.6× 545 0.3× 273 9.1k
Lynn C. Hartmann United States 59 4.9k 1.4× 2.9k 0.8× 4.4k 1.3× 3.2k 1.3× 759 0.4× 203 13.0k
Michael S. Sabel United States 51 3.8k 1.1× 1.4k 0.4× 2.7k 0.8× 3.0k 1.2× 411 0.2× 174 8.8k
Joseph A. Sparano United States 64 10.4k 2.9× 2.3k 0.7× 5.3k 1.5× 3.7k 1.5× 417 0.2× 397 15.9k
Seema A. Khan United States 41 2.5k 0.7× 1.7k 0.5× 3.2k 0.9× 1.4k 0.6× 345 0.2× 229 7.3k
Harry Hollema Netherlands 61 4.0k 1.1× 2.3k 0.7× 2.3k 0.7× 3.5k 1.4× 296 0.1× 229 11.9k
Anthony Lucci United States 56 5.5k 1.5× 2.3k 0.7× 6.1k 1.8× 3.2k 1.3× 272 0.1× 254 11.3k
Xavier Matías‐Guiu Spain 64 4.2k 1.2× 2.4k 0.7× 3.5k 1.0× 6.4k 2.6× 292 0.1× 435 16.1k

Countries citing papers authored by Gary M. Tse

Since Specialization
Citations

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

Fields of papers citing papers by Gary M. Tse

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gary M. Tse. 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 Gary M. Tse. The network helps show where Gary M. Tse may publish in the future.

Co-authorship network of co-authors of Gary M. Tse

This figure shows the co-authorship network connecting the top 25 collaborators of Gary M. Tse. A scholar is included among the top collaborators of Gary M. Tse 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 Gary M. Tse. Gary M. Tse 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.
Goh, Choon‐Hian, et al.. (2025). Clinical Application of Artificial Intelligence in the Diagnosis, Prediction, and Classification of Coronary Heart Disease. Cardiovascular Innovations and Applications. 10(1).
2.
Zheng, Xi, et al.. (2025). Ray-Aided Quadruple Affiliation Network for Calculating Tumor-Stroma Ratios in Breast Cancers. IEEE Transactions on Image Processing. 34. 2811–2825.
3.
Chen, Bonan, Jialin Wu, Guoming Chen, et al.. (2025). Antibody–drug conjugates in cancer therapy: current landscape, challenges, and future directions. Molecular Cancer. 24(1). 279–279. 1 indexed citations
4.
Hou, Zhihui, Liang Xu, Yihang Li, et al.. (2025). Heart Function, Valvular Hemodynamics, and Early-term Outcomes of Possible Thrombosis in Self-Expanding Transcatheter Pulmonary Valve. Canadian Journal of Cardiology. 41(10). 1885–1895.
5.
Tse, Gary M., et al.. (2024). Asperuloside activates hepatic NRF2 signaling to stimulate mitochondrial metabolism and restore lipid homeostasis in high fat diet-induced MAFLD. European Journal of Pharmacology. 983. 177003–177003. 7 indexed citations
6.
Li, Qing, Huixian Li, William C. Cho, et al.. (2024). TRPV2 calcium channel promotes breast cancer progression potential by activating autophagy. Cancer Cell International. 24(1). 324–324. 3 indexed citations
7.
Gao, Ping, et al.. (2024). Elevation of circulating FGF23 in chronic kidney disease promotes atrial fibrosis through the AKT pathway. European Heart Journal. 45(Supplement_1). 1 indexed citations
9.
Chou, Oscar Hou In, Jing Ning, Cheuk To Chung, et al.. (2024). Lower Risks of New-Onset Hepatocellular Carcinoma in Patients With Type 2 Diabetes Mellitus Treated With SGLT2 Inhibitors Versus DPP4 Inhibitors. Journal of the National Comprehensive Cancer Network. 22(2D). 12 indexed citations
10.
Lee, Conrad, et al.. (2024). Clinical values of nuclear morphometric analysis in fibroepithelial lesions. Breast Cancer Research. 26(1). 156–156.
11.
Xie, Fuda, Bonan Chen, Wei Chen, et al.. (2023). The nerve cells in gastrointestinal cancers: from molecular mechanisms to clinical intervention. Oncogene. 43(2). 77–91. 14 indexed citations
12.
13.
Li, Joshua, et al.. (2022). Comparison of GATA3, GCDFP15, Mammaglobin and SOX10 Immunocytochemistry in Aspirates of Metastatic Breast Cancer. SHILAP Revista de lepidopterología. 3(4). 219–227. 3 indexed citations
15.
Thennavan, Aatish, Francisco Beça, Susana García‐Recio, et al.. (2021). Molecular analysis of TCGA breast cancer histologic types. Cell Genomics. 1(3). 100067–100067. 62 indexed citations
16.
Tsang, Julia Y., et al.. (2020). INSM1 is a novel prognostic neuroendocrine marker for luminal B breast cancer. Pathology. 53(2). 170–178. 17 indexed citations
17.
Liu, Ming, Julia Y. Tsang, Michelle Lee, et al.. (2018). CD147 expression is associated with poor overall survival in chemotherapy treated triple-negative breast cancer. Journal of Clinical Pathology. 71(11). 1007–1014. 12 indexed citations
18.
Co, Michael, Clement Chen, Julia Y. Tsang, Gary M. Tse, & Ava Kwong. (2017). Mammary phyllodes tumour: a 15-year multicentre clinical review. Journal of Clinical Pathology. 71(6). 493–497. 33 indexed citations
19.
Zhang, Hong, Julia Y. Tsang, Yun‐Bi Ni, et al.. (2016). Hyaluronan synthase 2 is an adverse prognostic marker in androgen receptor-negative breast cancer. Journal of Clinical Pathology. 69(12). 1055–1062. 9 indexed citations
20.
Yue, Grace Gar‐Lee, Kwok‐Pui Fung, Gary M. Tse, Ping‐Chung Leung, & Clara Bik‐San Lau. (2006). Comparative Studies of Various Ganoderma Species and Their Different Parts with Regard to Their Antitumor and Immunomodulating Activities In Vitro. The Journal of Alternative and Complementary Medicine. 12(8). 777–789. 53 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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