Tony G. Willis

2.4k total citations · 1 hit paper
17 papers, 1.8k citations indexed

About

Tony G. Willis is a scholar working on Genetics, Pathology and Forensic Medicine and Molecular Biology. According to data from OpenAlex, Tony G. Willis has authored 17 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Genetics, 7 papers in Pathology and Forensic Medicine and 5 papers in Molecular Biology. Recurrent topics in Tony G. Willis's work include Chronic Lymphocytic Leukemia Research (10 papers), Lymphoma Diagnosis and Treatment (7 papers) and Glycosylation and Glycoproteins Research (4 papers). Tony G. Willis is often cited by papers focused on Chronic Lymphocytic Leukemia Research (10 papers), Lymphoma Diagnosis and Treatment (7 papers) and Glycosylation and Glycoproteins Research (4 papers). Tony G. Willis collaborates with scholars based in United Kingdom, Germany and Belgium. Tony G. Willis's co-authors include Martin J.S. Dyer, Iwona Włodarska, Peter G. Isaacson, Ming‐Qing Du, Loraine Karran, Munah Abdul-Rauf, Helen P. Price, Huaizheng Peng, Rifat Hamoudi and Amanda R. Perry and has published in prestigious journals such as Cell, Blood and American Journal Of Pathology.

In The Last Decade

Tony G. Willis

17 papers receiving 1.8k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Bcl10 Is Involved in t(1;14)(p22;q32) of MALT B Cell Lymphoma and Mutated in Multiple Tumor Types

1999 521 citations Tony G. Willis, Dalal Jadayel et al. Cell profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tony G. Willis United Kingdom	13	890	675	553	526	513	17	1.8k
R. D. Gascoyne Canada	24	925 1.0×	535 0.8×	599 1.1×	684 1.3×	389 0.8×	39	1.9k
K Offit United States	8	974 1.1×	558 0.8×	362 0.7×	765 1.5×	492 1.0×	10	1.8k
H Griesser Germany	17	899 1.0×	416 0.6×	332 0.6×	514 1.0×	535 1.0×	33	1.6k
Inga Vater Germany	21	701 0.8×	594 0.9×	315 0.6×	377 0.7×	324 0.6×	35	1.6k
D M Knowles United States	18	1.1k 1.3×	459 0.7×	598 1.1×	1.0k 1.9×	380 0.7×	23	1.9k
Dominique Leroux France	24	912 1.0×	880 1.3×	660 1.2×	841 1.6×	516 1.0×	74	2.4k
Massimo Degan Italy	24	900 1.0×	467 0.7×	764 1.4×	603 1.1×	826 1.6×	67	1.9k
Robert Coupland Canada	22	844 0.9×	429 0.6×	477 0.9×	683 1.3×	241 0.5×	40	1.7k
Magda Pinyol Spain	28	1.6k 1.8×	1.1k 1.6×	799 1.4×	1.3k 2.5×	302 0.6×	42	2.7k
M. Schaadt Germany	19	928 1.0×	449 0.7×	340 0.6×	571 1.1×	809 1.6×	46	1.9k

Countries citing papers authored by Tony G. Willis

Since Specialization

Citations

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

Fields of papers citing papers by Tony G. Willis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tony G. Willis. 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 Tony G. Willis. The network helps show where Tony G. Willis may publish in the future.

Co-authorship network of co-authors of Tony G. Willis

This figure shows the co-authorship network connecting the top 25 collaborators of Tony G. Willis. A scholar is included among the top collaborators of Tony G. Willis 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 Tony G. Willis. Tony G. Willis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

1.

Sonoki, Takashi, Tony G. Willis, David Oscier, et al.. (2004). Rapid amplification of immunoglobulin heavy chain switch (IGHS) translocation breakpoints using long-distance inverse PCR. Leukemia. 18(12). 2026–2031. 23 indexed citations

2.

Boyd, Robert S., Paul J. Adam, Sonal Patel, et al.. (2003). Proteomic analysis of the cell-surface membrane in chronic lymphocytic leukemia: identification of two novel proteins, BCNP1 and MIG2B. Leukemia. 17(8). 1605–1612. 56 indexed citations

3.

Sonoki, Takashi, Lana Harder, Doug Horsman, et al.. (2001). Cyclin D3 is a target gene of t(6;14)(p21.1;q32.3) of mature B-cell malignancies. Blood. 98(9). 2837–2844. 88 indexed citations

4.

Satterwhite, Ed, Takashi Sonoki, Tony G. Willis, et al.. (2001). The BCL11 gene family: involvement of BCL11A in lymphoid malignancies. Blood. 98(12). 3413–3420. 242 indexed citations

5.

Włodarska, Iwona, et al.. (2000). Identification of a 3 cM commonly deleted region in mantle cell lymphoma with chromosome 1P deletion. Blood. 96. 1 indexed citations

6.

Ye, Hongtao, Ahmet Doǧan, Loraine Karran, et al.. (2000). BCL10 Expression in Normal and Neoplastic Lymphoid Tissue. American Journal Of Pathology. 157(4). 1147–1154. 140 indexed citations

7.

Willis, Tony G. & Martin J.S. Dyer. (2000). The role of immunoglobulin translocations in the pathogenesis of B-cell malignancies. Blood. 96(3). 808–822. 195 indexed citations

8.

Du, Ming‐Qing, Huaizheng Peng, Hongxiang Liu, et al.. (2000). BCL10 gene mutation in lymphoma. Blood. 95(12). 3885–3890. 84 indexed citations

9.

Willis, Tony G. & Martin J.S. Dyer. (2000). The role of immunoglobulin translocations in the pathogenesis of B-cell malignancies. Blood. 96(3). 808–822. 181 indexed citations

10.

Du, Ming‐Qing, Huaizheng Peng, Hongxiang Liu, et al.. (2000). BCL10 gene mutation in lymphoma. Blood. 95(12). 3885–3890. 5 indexed citations

11.

Dyer, Martin J.S., Helen P. Price, Dalal Jadayel, et al.. (1999). In Response to Fakruddin et al. and Apostolou et al. Cell. 97(6). 686–688. 16 indexed citations

12.

Willis, Tony G., Dalal Jadayel, Ming‐Qing Du, et al.. (1999). Bcl10 Is Involved in t(1;14)(p22;q32) of MALT B Cell Lymphoma and Mutated in Multiple Tumor Types. Cell. 96(1). 35–45. 521 indexed citations breakdown →

13.

Gill, Sandra, Sarah Jefferies, Pinchas Osin, et al.. (1999). BCL10 is rarely mutated in human prostate carcinoma, small-cell lung cancer, head and neck tumours, renal carcinoma and sarcomas. British Journal of Cancer. 80(10). 1565–1568. 26 indexed citations

14.

Willis, Tony G., Ilana Zalcberg, Lionel Coignet, et al.. (1998). Molecular Cloning of Translocation t(1;14)(q21;q32) Defines a Novel Gene (BCL9) at Chromosome 1q21. Blood. 91(6). 1873–1881. 137 indexed citations

15.

Willis, Tony G., Ilana Zalcberg, Lionel Coignet, et al.. (1998). Molecular Cloning of Translocation t(1;14)(q21;q32) Defines a Novel Gene (BCL9) at Chromosome 1q21. Blood. 91(6). 1873–1881. 5 indexed citations

16.

Willis, Tony G., D Jadayel, Lionel Coignet, et al.. (1997). Rapid Molecular Cloning of Rearrangements of the IGHJ Locus Using Long-Distance Inverse Polymerase Chain Reaction. Blood. 90(6). 2456–2464. 108 indexed citations

17.

Willis, Tony G., D Jadayel, Lionel Coignet, et al.. (1997). Rapid Molecular Cloning of Rearrangements of the IGHJ Locus Using Long-Distance Inverse Polymerase Chain Reaction. Blood. 90(6). 2456–2464. 12 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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