Michel de Weers

2.4k total citations · 1 hit paper
28 papers, 1.7k citations indexed

About

Michel de Weers is a scholar working on Hematology, Immunology and Genetics. According to data from OpenAlex, Michel de Weers has authored 28 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Hematology, 13 papers in Immunology and 10 papers in Genetics. Recurrent topics in Michel de Weers's work include Multiple Myeloma Research and Treatments (14 papers), Chronic Lymphocytic Leukemia Research (10 papers) and Monoclonal and Polyclonal Antibodies Research (10 papers). Michel de Weers is often cited by papers focused on Multiple Myeloma Research and Treatments (14 papers), Chronic Lymphocytic Leukemia Research (10 papers) and Monoclonal and Polyclonal Antibodies Research (10 papers). Michel de Weers collaborates with scholars based in Netherlands, United States and Germany. Michel de Weers's co-authors include Paul W.H.I. Parren, Ruud K. B. Schuurman, Joost M. Bakker, Tuna Mutis, Michael S. van der Veer, Rudi W. Hendriks, Rob G.J. Mensink, Margriet E. M. Kraakman, Jan G. J. van de Winkel and Kenneth C. Anderson and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Clinical Oncology and Blood.

In The Last Decade

Michel de Weers

25 papers receiving 1.7k citations

Hit Papers

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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.

Daratumumab, a Novel Therapeutic Human CD38 Monoclonal Antibody, Induces Killing of Multiple Myeloma and Other Hematological Tumors

2010 746 citations Michel de Weers, Yu‐Tzu Tai et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Michel de Weers	1.0k	720	657	618	509	28	1.7k
Parul Doshi	745 0.7×	454 0.6×	787 1.2×	568 0.9×	296 0.6×	49	1.6k
Jeroen J. Lammerts van Bueren	899 0.9×	689 1.0×	1.2k 1.8×	850 1.4×	363 0.7×	24	2.2k
Inger S. Nijhof	1.3k 1.3×	555 0.8×	1.0k 1.6×	737 1.2×	292 0.6×	37	1.9k
Berris van Kessel	963 0.9×	438 0.6×	793 1.2×	573 0.9×	213 0.4×	33	1.5k
Kenneth Wen	740 0.7×	427 0.6×	787 1.2×	570 0.9×	71 0.1×	52	1.3k
Tinisha McDonald	868 0.9×	361 0.5×	543 0.8×	753 1.2×	372 0.7×	30	1.7k
Gema Mateo	1.7k 1.7×	308 0.4×	814 1.2×	1.2k 2.0×	661 1.3×	32	2.2k
Christina Krupka	502 0.5×	594 0.8×	861 1.3×	398 0.6×	86 0.2×	25	1.3k
Paola Circosta	259 0.3×	547 0.8×	304 0.5×	649 1.1×	638 1.3×	35	1.5k
Mariela Sivina	471 0.5×	778 1.1×	367 0.6×	649 1.1×	1.5k 2.9×	39	2.0k

Countries citing papers authored by Michel de Weers

Since Specialization

Citations

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

Fields of papers citing papers by Michel de Weers

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michel de Weers. 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 Michel de Weers. The network helps show where Michel de Weers may publish in the future.

Co-authorship network of co-authors of Michel de Weers

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

All Works

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20 of 20 papers shown

Nijhof, Inger S., Jeroen Lammerts van Bueren, Berris van Kessel, et al.. (2012). Towards CD38-Targeted Immunochemotherapy of Multiple Myeloma: Significant Improvement of Anti-Myeloma Effects of Lenalidomide, Bortezomib and Dexamethason by CD38-Specific Antibody Daratumumab, Even in Chemotherapy Resistant/Intolerant Patients. Blood. 120(21). 4988–4988. 1 indexed citations

Beum, Paul V., Margaret A. Lindorfer, P. Todd Stukenberg, et al.. (2011). Penetration of antibody‐opsonized cells by the membrane attack complex of complement promotes Ca²⁺influx and induces streamers. European Journal of Immunology. 41(8). 2436–2446. 27 indexed citations

Veer, Michael S. van der, Michel de Weers, Berris van Kessel, et al.. (2011). The therapeutic human CD38 antibody daratumumab improves the anti-myeloma effect of newly emerging multi-drug therapies. Blood Cancer Journal. 1(10). e41–e41. 81 indexed citations

Weers, Michel de, Berris van Kessel, Joost M. Bakker, et al.. (2011). The Human CD38 Monoclonal Antibody Daratumumab Improves the Anti-Myeloma Effect of Lenalidomide with Dexamethasone in Vitro. Blood. 118(21). 5106–5106.

Nijhof, Inger S., Michel de Weers, Pascale André, et al.. (2011). Synergistic Action of the Human Inhibitory KIR Antibody IPH2102, and the Human CD38 Antibody Daratumumab to Enhance the Lysis of Primary Multiple Myeloma (MM) Cells in the Bone Marrow Mononuclear Cells (MNCs) From Myeloma Patients. Blood. 118(21). 1865–1865. 3 indexed citations

Veer, Michael S. van der, Michel de Weers, Berris van Kessel, et al.. (2010). Towards effective immunotherapy of myeloma: enhanced elimination of myeloma cells by combination of lenalidomide with the human CD38 monoclonal antibody daratumumab. Haematologica. 96(2). 284–290. 168 indexed citations

Groen, Richard W.J., Frans M.A. Hofhuis, Berris van Kessel, et al.. (2010). A Unique New Humanized Mouse Model for Multiple Myeloma (MM): Opportunities for Studying MM In Its Natural Environment and for Preclinical Testing. Blood. 116(21). 981–981. 1 indexed citations

Veer, Michael S. van der, Michel de Weers, Berris van Kessel, et al.. (2010). Improved Myeloma Targeting by Combination of the Human Anti-CD38 Antibody Daratumumab with Lenalidomide and Bortezomib. Blood. 116(21). 3030–3030. 2 indexed citations

Groen, Richard W.J., Frans M.A. Hofhuis, Berris van Kessel, et al.. (2010). In Vitro and In Vivo Efficacy of CD38 Directed Therapy with Daratumumab In the Treatment of Multiple Myeloma. Blood. 116(21). 3058–3058. 3 indexed citations

10.

Veer, Michael S. van der, Michel de Weers, Berris van Kessel, et al.. (2010). Towards Effective Immunotherapy of Multiple Myeloma: Enhanced Elimination of Myeloma Cells by Combination of Lenalidomide with the Human CD38 Monoclonal Antibody Daratumumab. Blood. 116(21). 3059–3059. 1 indexed citations

11.

Kong, Sun‐Young, Xianfeng Li, Sabikun Nahar, et al.. (2010). Daratumumab Directly Induces Human Multiple Myeloma Cell Death and Acts Synergistically with Conventional and Novel Anti-Myeloma Drugs. Blood. 116(21). 3013–3013. 6 indexed citations

12.

Gerritsen, Arnout F., Martijn Bosch, Michel de Weers, Jan G. J. van de Winkel, & Paul W.H.I. Parren. (2009). High throughput screening for antibody induced complement-dependent cytotoxicity in early antibody discovery using homogeneous macroconfocal fluorescence imaging. Journal of Immunological Methods. 352(1-2). 140–146. 6 indexed citations

13.

Groen, Richard W.J., Frans M.A. Hofhuis, Henk‐Jan Prins, et al.. (2009). The Humanized Multiple Myeloma Mouse Model: Opportunities for Studying the Pathogenesis of MM in Its Natural Environment.. Blood. 114(22). 1847–1847.

14.

Kraakman, Margriet E. M., et al.. (1995). Identification of a CD40L gene mutation and genetic counselling in a family with immunodeficiency with hyperimmunoglobulinemia M. Clinical Genetics. 48(1). 46–48. 6 indexed citations

15.

Weers, Michel de, Rob G.J. Mensink, Margriet E. M. Kraakman, Ruud K. B. Schuurman, & Rudi W. Hendriks. (1994). Mutation analysis of the Bruton's tyrosine kinase gene in X-linked agammaglobulinemia: identification of a mutation which affects the same codon as is altered in immunodeficient xid mice. Human Molecular Genetics. 3(1). 161–166. 106 indexed citations

16.

Kenter, Marcel, Ńel Otting, Michel de Weers, et al.. (1993). Mhc-DRB and-DQA1 nucleotide sequences of three lowland gorillas. Human Immunology. 36(4). 205–218. 20 indexed citations

17.

Weers, Michel de, Martie C. M. Verschuren, Margriet E. M. Kraakman, et al.. (1993). The Bruton's tyrosine kinase gene is expressed throughout B cell differentiation, from early precursor B cell stages preceding immunoglobulin gene rearrangement up to mature B cell stages. European Journal of Immunology. 23(12). 3109–3114. 168 indexed citations

18.

Weers, Michel de, Rob G.J. Mensink, Marcel Kenter, & Ruud K. B. Schuurman. (1992). Three dinucleotide repeat polymorphisms at the DXS178 locus. Human Molecular Genetics. 1(8). 653–653. 9 indexed citations

19.

Weers, Michel de, et al.. (1991). X-linked agammaglobulinemia. Clinical Immunology and Immunopathology. 61(2). S83–S93. 20 indexed citations

20.

Hendriks, Rudi W., Michel de Weers, Rob G.J. Mensink, et al.. (1991). Diagnosis of Wiskott-Aldrich syndrome by analysis of the X chromosome inactivation patterns in maternal leucocyte populations using the hypervariable DXS255 locus.. PubMed. 84(2). 219–22. 21 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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