Wayne Tam

15.0k total citations · 1 hit paper
104 papers, 5.4k citations indexed

About

Wayne Tam is a scholar working on Pathology and Forensic Medicine, Molecular Biology and Genetics. According to data from OpenAlex, Wayne Tam has authored 104 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Pathology and Forensic Medicine, 36 papers in Molecular Biology and 31 papers in Genetics. Recurrent topics in Wayne Tam's work include Lymphoma Diagnosis and Treatment (53 papers), Acute Myeloid Leukemia Research (20 papers) and Chronic Lymphocytic Leukemia Research (17 papers). Wayne Tam is often cited by papers focused on Lymphoma Diagnosis and Treatment (53 papers), Acute Myeloid Leukemia Research (20 papers) and Chronic Lymphocytic Leukemia Research (17 papers). Wayne Tam collaborates with scholars based in United States, Italy and Singapore. Wayne Tam's co-authors include Amy Chadburn, James E. Dahlberg, Mario Gómez, Peggy S. Eis, Liping Sun, Zongdong Li, Elsebet Lund, W S Hayward, Dina Ben‐Yehuda and Daniel M. Knowles and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Clinical Investigation.

In The Last Decade

Wayne Tam

101 papers receiving 5.3k citations

Hit Papers

Accumulation of miR-155 and BIC RNA in human B cell lymph... 2005 2026 2012 2019 2005 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wayne Tam United States 34 3.0k 2.4k 1.3k 1.3k 982 104 5.4k
Sandeep S. Davé United States 28 1.8k 0.6× 942 0.4× 1.5k 1.1× 1.3k 1.0× 993 1.0× 93 4.0k
Pulivarthi H. Rao United States 52 4.2k 1.4× 1.4k 0.6× 2.5k 1.9× 1.5k 1.2× 695 0.7× 129 7.7k
Mathijs Baens Belgium 29 1.5k 0.5× 998 0.4× 959 0.7× 842 0.7× 1.2k 1.2× 51 3.7k
Jane Houldsworth United States 41 2.5k 0.8× 811 0.3× 1.2k 0.9× 1.1k 0.9× 596 0.6× 111 5.1k
Sibrand Poppema Netherlands 45 2.2k 0.7× 1.4k 0.6× 2.0k 1.5× 2.4k 1.9× 2.1k 2.2× 160 6.3k
Mariella Dono Italy 26 4.1k 1.4× 3.7k 1.6× 547 0.4× 992 0.8× 1.6k 1.6× 64 6.5k
Shimin Hu United States 32 1.7k 0.6× 557 0.2× 1.3k 1.0× 865 0.7× 909 0.9× 160 4.1k
Šárka Pospı́šilová Czechia 37 2.1k 0.7× 827 0.4× 1.0k 0.8× 1.0k 0.8× 1.2k 1.2× 181 4.4k
Stanislas du Manoir France 45 3.9k 1.3× 2.3k 1.0× 1.9k 1.4× 1.5k 1.2× 388 0.4× 85 8.0k
Roland P. Kuiper Netherlands 35 2.6k 0.9× 1.1k 0.5× 1.1k 0.8× 1.1k 0.8× 328 0.3× 123 5.4k

Countries citing papers authored by Wayne Tam

Since Specialization
Citations

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

Fields of papers citing papers by Wayne Tam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wayne Tam. 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 Wayne Tam. The network helps show where Wayne Tam may publish in the future.

Co-authorship network of co-authors of Wayne Tam

This figure shows the co-authorship network connecting the top 25 collaborators of Wayne Tam. A scholar is included among the top collaborators of Wayne Tam 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 Wayne Tam. Wayne Tam 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
2.
Chen, Weina, Adam Bagg, Prasad Koduru, et al.. (2023). Acute leukemias with complex karyotype show a similarly poor outcome independent of mixed, myeloid or lymphoblastic immunophenotype: A study from the Bone Marrow Pathology Group. Leukemia Research. 130. 107309–107309. 4 indexed citations
3.
Tang, Guilin, Wayne Tam, Nicholas J. Short, et al.. (2021). Myeloid/lymphoid neoplasms with FLT3 rearrangement. Modern Pathology. 34(9). 1673–1685. 25 indexed citations
4.
Zhou, Jingqi, Kui Nie, Shuhua Cheng, et al.. (2021). Oncogenic role of the SOX9-DHCR24-cholesterol biosynthesis axis in IGH-BCL2 + diffuse large B-cell lymphomas. Blood. 139(1). 73–86. 24 indexed citations
5.
Patel, Sanjay S., Julia T. Geyer, Joelle Racchumi, et al.. (2021). Comparison of Multiple Clinical Testing Modalities for Assessment of NPM1-Mutant AML. Frontiers in Oncology. 11. 701318–701318. 13 indexed citations
6.
Yiğit, Nuri, et al.. (2021). Merkel cell carcinoma in the setting of hematologic disease is associated with unique features and potential pitfalls. Annals of Diagnostic Pathology. 56. 151868–151868. 4 indexed citations
7.
Vaisitti, Tiziana, Esteban Braggio, John N. Allan, et al.. (2018). Novel Richter Syndrome Xenograft Models to Study Genetic Architecture, Biology, and Therapy Responses. Cancer Research. 78(13). 3413–3420. 33 indexed citations
8.
Swords, Ronan, Steven Coutré, Michael B. Maris, et al.. (2018). Pevonedistat, a first-in-class NEDD8-activating enzyme inhibitor, combined with azacitidine in patients with AML. Blood. 131(13). 1415–1424. 157 indexed citations
9.
Ruan, Jia, Peter Martin, Paul J. Christos, et al.. (2017). Initial Treatment with Lenalidomide Plus Rituximab for Mantle Cell Lymphoma: 5-Year Follow-up and Correlative Analysis from a Multi-Center Phase II Study. Blood. 130. 154–154. 7 indexed citations
10.
Orso, Francesca, Elena Grassi, Andrea Costamagna, et al.. (2017). Dysregulation of Blimp1 transcriptional repressor unleashes p130Cas/ErbB2 breast cancer invasion. Scientific Reports. 7(1). 1145–1145. 18 indexed citations
11.
Plesner, Trine Lindhardt, Martin Bjerregård Pedersen, Maja Ludvigsen, et al.. (2017). Lympho- and Myeloproliferative Malignancies Occurring in the Same Host: Description of a Nationwide Discovery Cohort. 1 indexed citations
12.
Wang, Sa A., Robert P. Hasserjian, Wayne Tam, et al.. (2017). Bone marrow morphology is a strong discriminator between chronic eosinophilic leukemia, not otherwise specified and reactive idiopathic hypereosinophilic syndrome. Haematologica. 102(8). 1352–1360. 50 indexed citations
14.
Mathew, Susan, Yifang Liu, Maria Gomez-Jenkins, et al.. (2015). Cyclin D1–Positive Diffuse Large B-Cell Lymphoma With IGH-CCND1 Translocation and BCL6 Rearrangement. American Journal of Clinical Pathology. 143(2). 288–299. 13 indexed citations
15.
Ma, Jiao, Kun Nie, David Redmond, et al.. (2015). EBV-miR-BHRF1-2 targets PRDM1/Blimp1: potential role in EBV lymphomagenesis. Leukemia. 30(3). 594–604. 55 indexed citations
16.
Ballon, Gianna, et al.. (2011). Kaposi sarcoma herpesvirus (KSHV) vFLIP oncoprotein induces B cell transdifferentiation and tumorigenesis in mice. Journal of Clinical Investigation. 121(3). 1141–1153. 81 indexed citations
17.
Mavrakis, Konstantinos J., Andrew L. Wolfe, Elisa Oricchio, et al.. (2010). Genome-wide RNA-mediated interference screen identifies miR-19 targets in Notch-induced T-cell acute lymphoblastic leukaemia. Nature Cell Biology. 12(4). 372–379. 275 indexed citations
18.
Zhang, Wenyong, James E. Dahlberg, & Wayne Tam. (2007). MicroRNAs in Tumorigenesis. American Journal Of Pathology. 171(3). 728–738. 178 indexed citations
19.
Tam, Wayne. (2001). Identification and characterization of human BIC, a gene on chromosome 21 that encodes a noncoding RNA. Gene. 274(1-2). 157–167. 218 indexed citations
20.
Tam, Wayne, Dina Ben‐Yehuda, & W S Hayward. (1997). bic , a Novel Gene Activated by Proviral Insertions in Avian Leukosis Virus-Induced Lymphomas, Is Likely To Function through Its Noncoding RNA. Molecular and Cellular Biology. 17(3). 1490–1502. 238 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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