Meyling Cheok

6.5k total citations · 2 hit papers
55 papers, 3.1k citations indexed

About

Meyling Cheok is a scholar working on Molecular Biology, Hematology and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Meyling Cheok has authored 55 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 27 papers in Hematology and 24 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Meyling Cheok's work include Acute Myeloid Leukemia Research (25 papers), Acute Lymphoblastic Leukemia research (23 papers) and Childhood Cancer Survivors' Quality of Life (9 papers). Meyling Cheok is often cited by papers focused on Acute Myeloid Leukemia Research (25 papers), Acute Lymphoblastic Leukemia research (23 papers) and Childhood Cancer Survivors' Quality of Life (9 papers). Meyling Cheok collaborates with scholars based in France, United States and Germany. Meyling Cheok's co-authors include William E. Evans, Mary V. Relling, Wenjian Yang, Ching‐Hon Pui, Monique L. den Boer, Rob Pieters, Gritta Janka‐Schaub, Cheng Cheng, Deqing Pei and Amy de Haar-Holleman and has published in prestigious journals such as New England Journal of Medicine, Journal of Clinical Investigation and Nature Medicine.

In The Last Decade

Meyling Cheok

54 papers receiving 3.1k citations

Hit Papers

A subtype of childhood acute lymphoblastic leukaemia with... 2009 2026 2014 2020 2009 2022 100 200 300 400 500

Peers

Meyling Cheok
John E. Godwin United States
William Blum United States
Ralph Wäsch Germany
Rose Ann Padua United Kingdom
B. Douglas Smith United States
Virginia M. Klimek United States
Meyling Cheok
Citations per year, relative to Meyling Cheok Meyling Cheok (= 1×) peers Hansjörg Riehm

Countries citing papers authored by Meyling Cheok

Since Specialization
Citations

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

Fields of papers citing papers by Meyling Cheok

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meyling Cheok

This figure shows the co-authorship network connecting the top 25 collaborators of Meyling Cheok. A scholar is included among the top collaborators of Meyling Cheok 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 Meyling Cheok. Meyling Cheok 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.
Lapillonne, Hélène, Meyling Cheok, Claude Preudhomme, et al.. (2022). Prognostic impact of ABCA3 expression in adult and pediatric acute myeloid leukemia: an ALFA-ELAM02 joint study. Blood Advances. 6(9). 2773–2777. 5 indexed citations
2.
Jouy, Nathalie, Valério Farfariello, Thierry Idziorek, et al.. (2022). Involvement of ORAI1/SOCE in Human AML Cell Lines and Primary Cells According to ABCB1 Activity, LSC Compartment and Potential Resistance to Ara-C Exposure. International Journal of Molecular Sciences. 23(10). 5555–5555. 5 indexed citations
3.
Zeng, Andy G.X., Liqing Jin, Amanda Mitchell, et al.. (2022). A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nature Medicine. 28(6). 1212–1223. 150 indexed citations breakdown →
4.
Nehme, Ali, Frédéric Picou, Meyling Cheok, et al.. (2020). Horizontal meta-analysis identifies common deregulated genes across AML subgroups providing a robust prognostic signature. Blood Advances. 4(20). 5322–5335. 10 indexed citations
5.
Boyer, Thomas, Adeline Barthélémy, Alice Marceau‐Renaut, et al.. (2019). Clinical Significance of ABCB1 in Acute Myeloid Leukemia: A Comprehensive Study. Cancers. 11(9). 1323–1323. 29 indexed citations
6.
Boyer, Thomas, Adriana Pleșa, Pauline Peyrouze, et al.. (2018). Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up. Journal of Visualized Experiments. 10 indexed citations
7.
Boyer, Thomas, Pauline Peyrouze, Soizic Guihard, et al.. (2017). Targeting Aberrant Expression of CD81 Impacts Cell Adhesion and Migration, Drug Resistance and Prognosis of Acute Myeloid Leukemia. Blood. 130. 2675–2675. 3 indexed citations
8.
Fu, Qiangwei, Soizic Guihard, Meyling Cheok, et al.. (2015). CD38 in Hairy Cell Leukemia Is a Marker of Poor Prognosis and a New Target for Therapy. Cancer Research. 75(18). 3902–3911. 37 indexed citations
9.
Célisse, Alain, et al.. (2014). MPAgenomics: an R package for multi-patient analysis of genomic markers. BMC Bioinformatics. 15(1). 394–394. 2 indexed citations
10.
Zaza, Gianluigi, Meyling Cheok, Natalia F. Krynetskaia, et al.. (2010). Thiopurine pathway. Pharmacogenetics and Genomics. 20(9). 573–574. 85 indexed citations
11.
Boer, Monique L. den, Marjon van Slegtenhorst, Renée X. de Menezes, et al.. (2009). A subtype of childhood acute lymphoblastic leukaemia with poor treatment outcome: a genome-wide classification study. The Lancet Oncology. 10(2). 125–134. 595 indexed citations breakdown →
12.
Krishnamurthy, Partha, Matthias Schwab, Kazumasa Takenaka, et al.. (2008). Transporter-Mediated Protection against Thiopurine-Induced Hematopoietic Toxicity. Cancer Research. 68(13). 4983–4989. 116 indexed citations
13.
Cheok, Meyling, Nicolas Pottier, Leo Kager, & William E. Evans. (2008). Pharmacogenetics in Acute Lymphoblastic Leukemia. Seminars in Hematology. 46(1). 39–51. 52 indexed citations
14.
Pottier, Nicolas, Meyling Cheok, & Leo Kager. (2008). Antileukemic drug effects in childhood acute lymphoblastic leukemia. Expert Review of Clinical Pharmacology. 1(3). 401–413. 1 indexed citations
15.
Sorich, Michael J., Nicolas Pottier, Deqing Pei, et al.. (2008). In Vivo Response to Methotrexate Forecasts Outcome of Acute Lymphoblastic Leukemia and Has a Distinct Gene Expression Profile. PLoS Medicine. 5(4). e83–e83. 52 indexed citations
16.
Pottier, Nicolas, Wenjian Yang, Mahfoud Assem, et al.. (2008). The SWI/SNF Chromatin-Remodeling Complex and Glucocorticoid Resistance in Acute Lymphoblastic Leukemia. JNCI Journal of the National Cancer Institute. 100(24). 1792–1803. 47 indexed citations
17.
Pottier, Nicolas, Meyling Cheok, Wentao Yang, et al.. (2007). Expression of SMARCB1 modulates steroid sensitivity in human lymphoblastoid cells: identification of a promoter snp that alters PARP1 binding and SMARCB1 expression. Human Molecular Genetics. 16(19). 2261–2271. 34 indexed citations
18.
Cheok, Meyling & William E. Evans. (2006). Acute lymphoblastic leukaemia: a model for the pharmacogenomics of cancer therapy. Nature reviews. Cancer. 6(2). 117–129. 167 indexed citations
19.
Kager, Leo, Meyling Cheok, Wenjian Yang, et al.. (2005). Folate pathway gene expression differs in subtypes of acute lymphoblastic leukemia and influences methotrexate pharmacodynamics. Journal of Clinical Investigation. 115(1). 110–117. 109 indexed citations
20.
Panetta, John C., William E. Evans, & Meyling Cheok. (2005). Mechanistic mathematical modelling of mercaptopurine effects on cell cycle of human acute lymphoblastic leukaemia cells. British Journal of Cancer. 94(1). 93–100. 34 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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