Soo‐Yong Tan

5.8k total citations
106 papers, 2.6k citations indexed

About

Soo‐Yong Tan is a scholar working on Pathology and Forensic Medicine, Molecular Biology and Oncology. According to data from OpenAlex, Soo‐Yong Tan has authored 106 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Pathology and Forensic Medicine, 40 papers in Molecular Biology and 32 papers in Oncology. Recurrent topics in Soo‐Yong Tan's work include Lymphoma Diagnosis and Treatment (42 papers), Viral-associated cancers and disorders (16 papers) and Chronic Lymphocytic Leukemia Research (13 papers). Soo‐Yong Tan is often cited by papers focused on Lymphoma Diagnosis and Treatment (42 papers), Viral-associated cancers and disorders (16 papers) and Chronic Lymphocytic Leukemia Research (13 papers). Soo‐Yong Tan collaborates with scholars based in Singapore, United Kingdom and United States. Soo‐Yong Tan's co-authors include Mark B. Pepys, Soon Thye Lim, Siok‐Bian Ng, Oi Lian Kon, Charles Chuah, Keng Boon Wee, Wei Xia Ang, Motomi Osato, Bruno Amati and Cheryl M. Koh and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and The Lancet.

In The Last Decade

Soo‐Yong Tan

102 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Soo‐Yong Tan Singapore 27 1.2k 686 617 315 301 106 2.6k
Walter E. Aulitzky Germany 34 1.4k 1.1× 1.1k 1.6× 420 0.7× 385 1.2× 692 2.3× 121 3.6k
Karen Seiter United States 30 1.2k 0.9× 1.1k 1.6× 309 0.5× 146 0.5× 342 1.1× 137 2.8k
Heinz Sill Austria 32 1.2k 0.9× 912 1.3× 438 0.7× 795 2.5× 568 1.9× 137 3.3k
Samuel A. Jacobs United States 25 620 0.5× 1.4k 2.0× 753 1.2× 329 1.0× 356 1.2× 143 2.9k
Jianmin Wang China 26 820 0.7× 700 1.0× 222 0.4× 229 0.7× 429 1.4× 141 2.7k
Martha Arellano United States 27 2.0k 1.6× 848 1.2× 232 0.4× 312 1.0× 720 2.4× 119 4.4k
Charalambos Andreadis United States 27 1.5k 1.2× 1.4k 2.0× 650 1.1× 1.0k 3.3× 179 0.6× 127 3.3k
Eleftheria Hatzimichael Greece 30 1.6k 1.3× 554 0.8× 285 0.5× 907 2.9× 565 1.9× 114 3.0k
Jan Eucker Germany 29 1.0k 0.8× 760 1.1× 308 0.5× 284 0.9× 332 1.1× 85 2.5k
Kylie D. Mason Australia 18 2.4k 1.9× 809 1.2× 505 0.8× 247 0.8× 588 2.0× 58 4.0k

Countries citing papers authored by Soo‐Yong Tan

Since Specialization
Citations

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

Fields of papers citing papers by Soo‐Yong Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Soo‐Yong Tan. 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 Soo‐Yong Tan. The network helps show where Soo‐Yong Tan may publish in the future.

Co-authorship network of co-authors of Soo‐Yong Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Soo‐Yong Tan. A scholar is included among the top collaborators of Soo‐Yong Tan 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 Soo‐Yong Tan. Soo‐Yong Tan 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.
He, Cheng, et al.. (2024). MicroRNA expression signature as a biomarker in the diagnosis of nodal T-cell lymphomas. Cancer Cell International. 24(1). 48–48. 1 indexed citations
2.
Wang, Rong, Lan Zhang, Jun Wang, et al.. (2024). Prebiotic stachyose inhibits PRDX5 activity and castration-resistant prostate cancer development. International Journal of Biological Macromolecules. 278(Pt 3). 134844–134844. 2 indexed citations
3.
Ding, Ling‐Wen, Henry Yang, Jonathan W. Said, et al.. (2023). Targeting RNA Exonuclease XRN1 Potentiates Efficacy of Cancer Immunotherapy. Cancer Research. 83(6). 922–938. 12 indexed citations
4.
Tan, Tze King, Yan Li, Zhiyuan Gong, et al.. (2022). IRF4 drives clonal evolution and lineage choice in a zebrafish model of T-cell lymphoma. Nature Communications. 13(1). 2420–2420. 8 indexed citations
5.
Ye, Zhiyong, Alice M.S. Cheung, Yufen Goh, et al.. (2021). Effective Killing of Acute Myeloid Leukemia by TIM-3 Targeted Chimeric Antigen Receptor T Cells. Molecular Cancer Therapeutics. 20(9). 1702–1712. 37 indexed citations
6.
Beauchamp, Erwan, Megan C. Yap, Aishwarya Iyer, et al.. (2020). Targeting N-myristoylation for therapy of B-cell lymphomas. Nature Communications. 11(1). 5348–5348. 50 indexed citations
7.
Liew, Su May, et al.. (2019). Clinical significance of aberrant microRNAs expression in predicting disease relapse/refractoriness to treatment in diffuse large B-cell lymphoma: A meta-analysis. Critical Reviews in Oncology/Hematology. 144. 102818–102818. 16 indexed citations
8.
Wengner, Antje M., Sabine Zitzmann-Kolbe, Sze Huey Tan, et al.. (2017). Simultaneous Inhibition of PI3Kδ and PI3Kα Induces ABC-DLBCL Regression by Blocking BCR-Dependent and -Independent Activation of NF-κB and AKT. Cancer Cell. 31(1). 64–78. 101 indexed citations
9.
10.
Tan, Daryl, Soo‐Yong Tan, Soon Thye Lim, et al.. (2013). Management of B-cell non-Hodgkin lymphoma in Asia: resource-stratified guidelines. The Lancet Oncology. 14(12). e548–e561. 24 indexed citations
11.
Tan, Soo‐Yong, Tiffany Tang, Daryl Tan, et al.. (2012). Relationship between atypical T- and B-cell size predicts survival in peripheral T-cell lymphomas with large B-cells. Pathology. 45(1). 28–37. 1 indexed citations
12.
Tan, Soo‐Yong, Tiffany Tang, Soon Thye Lim, et al.. (2012). Angioimmunoblastic T‐cell lymphoma with hyperplastic germinal centres (pattern 1) shows superior survival to patterns 2 and 3: a meta‐analysis of 56 cases. Histopathology. 60(4). 570–585. 12 indexed citations
13.
Tan, Soo‐Yong, et al.. (2011). Automated Segmentation and Measurement for Cancer Classification of HER2/neu Status in Breast Carcinomas. International Conference on Bioinformatics. 43–48. 1 indexed citations
14.
Thike, Aye Aye, Soo‐Yong Tan, Iain Beehuat Tan, et al.. (2010). Expression of heparan sulfate in gastric carcinoma and its correlation with clinicopathological features and patient survival. Journal of Clinical Pathology. 64(2). 153–158. 9 indexed citations
15.
16.
Nga, Min En, et al.. (2008). Malignant Adenomyoepithelial Tumor of the Breast: Multi-immunolabeling Technique and Detailed Immunophenotypic Study. Applied immunohistochemistry & molecular morphology. 16(1). 100–104. 5 indexed citations
17.
Kon, Oi Lian, Tai‐Tung Yip, Weng Hoong Chan, et al.. (2008). The distinctive gastric fluid proteome in gastric cancer reveals a multi-biomarker diagnostic profile. BMC Medical Genomics. 1(1). 54–54. 32 indexed citations
18.
Tedoldi, Sara, J L Cordell, Soo‐Yong Tan, et al.. (2006). Jaw1/LRMP, a germinal centre‐associated marker for the immunohistological study of B‐cell lymphomas. The Journal of Pathology. 209(4). 454–463. 33 indexed citations
19.
Gellrich, Sylke, Roland Ventura, Margaret T. Jones, Soo‐Yong Tan, & David Y. Mason. (2004). Immunofluorescent and FISH Analysis of Skin Biopsies. American Journal of Dermatopathology. 26(3). 242–247. 13 indexed citations
20.
Bhanumathy, Kalpana K., et al.. (1999). An Improved Model of Galactosamine-Induced Fulminant Hepatic Failure in the Pig. Journal of Surgical Research. 82(2). 121–130. 26 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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