Waldemar Debinski

8.5k citations
177 papers · 6.9k · h-index 46

Impact in

  • Immunology top 0.5%
    • Toxin Mechanisms and Immunotoxins
    • Immunotherapy and Immune Responses
    • Immune Cell Function and Interaction
  • Genetics top 1%
    • Glioma Diagnosis and Treatment
    • Virus-based gene therapy research

Papers in

Waldemar Debinski

175 papers receiving 6.8k citations

Peers

Waldemar Debinski
Comparison fields: 5 of 121
  • Immunology 2.3k
  • Genetics 862
  • Biotechnology 708
  • Oncology 1.5k
  • Cellular and Molecular Neuroscience 888
Replace Balveen Kaur with:
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Citations per field
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Citations per year

Countries citing papers authored by Waldemar Debinski

Since Specialization
Citations

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

Fields of papers citing papers by Waldemar Debinski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Waldemar Debinski, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Waldemar Debinski Line = papers co-authored together Waldemar Debinski links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 177 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018262
2 2015259
3 2008254
4 2005231
5 1995229
6
Receptor for interleukin 13 is a marker and therapeutic target for human high-grade gliomas.
1999216
7
Human glioma cells overexpress receptors for interleukin 13 and are extremely sensitive to a novel chimeric protein composed of interleukin 13 and pseudomonas exotoxin.
1995193
8 1995185
9 1997135
10 2009132
11 2000120
12 2002117
13 1997115
14 2008102
15 199899
16 199698
17 200587
18 198785
19 201583
20 200781

About Waldemar Debinski

Waldemar Debinski is a scholar working on Immunology, Molecular Biology, Biotechnology, Radiology, Nuclear Medicine and Imaging and Genetics, having authored 177 papers that have together received 6.9k indexed citations. Recurring topics across this work include Toxin Mechanisms and Immunotoxins (51 papers), Glioma Diagnosis and Treatment (28 papers), Monoclonal and Polyclonal Antibodies Research (24 papers), Heart Failure Treatment and Management (22 papers), Transgenic Plants and Applications (18 papers), Cancer Research and Treatments (15 papers), Immunotherapy and Immune Responses (14 papers) and CAR-T cell therapy research (13 papers). The work is most often cited by research in Immunology (2.3k citations), Genetics (862 citations), Biotechnology (708 citations), Oncology (1.5k citations) and Cellular and Molecular Neuroscience (888 citations). Waldemar Debinski has collaborated with scholars based in United States, Canada and Hungary. Frequent co-authors include Denise M. Gibo, Raj K. Puri, Nicholas I. Obiri, Jill Wykosky, Ira Pastan, Stephen B. Tatter, Akiva Mintz, Kounosuke Watabe, Hui‐Wen Lo and G. Yancey Gillespie. Their work appears in journals such as Neuro-Oncology, Journal of Biological Chemistry, Oncotarget, Cancer Research and Journal of Neuro-Oncology.

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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