Jonathan E. Kolitz

24.1k total citations · 3 hit papers
256 papers, 14.4k citations indexed

About

Jonathan E. Kolitz is a scholar working on Hematology, Genetics and Molecular Biology. According to data from OpenAlex, Jonathan E. Kolitz has authored 256 papers receiving a total of 14.4k indexed citations (citations by other indexed papers that have themselves been cited), including 150 papers in Hematology, 106 papers in Genetics and 84 papers in Molecular Biology. Recurrent topics in Jonathan E. Kolitz's work include Acute Myeloid Leukemia Research (136 papers), Chronic Lymphocytic Leukemia Research (82 papers) and Acute Lymphoblastic Leukemia research (57 papers). Jonathan E. Kolitz is often cited by papers focused on Acute Myeloid Leukemia Research (136 papers), Chronic Lymphocytic Leukemia Research (82 papers) and Acute Lymphoblastic Leukemia research (57 papers). Jonathan E. Kolitz collaborates with scholars based in United States, Italy and Germany. Jonathan E. Kolitz's co-authors include Richard A. Larson, R. Kanti, Steven L. Allen, Clara D. Bloomfield, Nicholas Chiorazzi, Bayard L. Powell, Krzysztof Mrózek, Guido Marcucci, Andrew J. Carroll and Rajendra N. Damle and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Jonathan E. Kolitz

243 papers receiving 14.1k citations

Hit Papers

Ig V Gene Mutation Status... 1999 2026 2008 2017 1999 2000 2010 500 1000 1.5k

Author Peers

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

Author Last Decade Papers Cites
Jonathan E. Kolitz 6.7k 6.6k 5.2k 4.0k 3.7k 256 14.4k
Gunnar Juliusson 4.4k 0.7× 3.3k 0.5× 2.9k 0.6× 2.8k 0.7× 2.3k 0.6× 189 9.0k
Konstanze Döhner 5.9k 0.9× 10.4k 1.6× 8.4k 1.6× 2.3k 0.6× 2.2k 0.6× 272 16.6k
Jan A. Burger 12.9k 1.9× 6.1k 0.9× 5.4k 1.1× 8.5k 2.1× 7.8k 2.1× 431 20.9k
Steven P. Treon 10.3k 1.5× 7.3k 1.1× 6.5k 1.3× 8.8k 2.2× 5.8k 1.6× 471 19.3k
Emili Montserrat 10.5k 1.6× 3.8k 0.6× 3.4k 0.7× 9.4k 2.4× 5.3k 1.4× 296 16.3k
Oliver G. Ottmann 5.0k 0.8× 10.4k 1.6× 4.9k 1.0× 882 0.2× 2.4k 0.6× 372 16.1k
Claude Preudhomme 4.4k 0.7× 9.4k 1.4× 5.7k 1.1× 1.3k 0.3× 1.3k 0.3× 356 13.3k
Nigel H. Russell 2.5k 0.4× 8.9k 1.3× 4.4k 0.8× 1.7k 0.4× 1.9k 0.5× 355 12.4k
Paolo Ghia 8.8k 1.3× 2.2k 0.3× 2.6k 0.5× 6.4k 1.6× 6.0k 1.6× 399 12.7k
Ramón García‐Sánz 3.1k 0.5× 5.5k 0.8× 4.0k 0.8× 2.4k 0.6× 1.4k 0.4× 337 9.6k

Countries citing papers authored by Jonathan E. Kolitz

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan E. Kolitz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan E. Kolitz. 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 Jonathan E. Kolitz. The network helps show where Jonathan E. Kolitz may publish in the future.

Co-authorship network of co-authors of Jonathan E. Kolitz

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan E. Kolitz. A scholar is included among the top collaborators of Jonathan E. Kolitz 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 Jonathan E. Kolitz. Jonathan E. Kolitz 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.
Nicolet, Deedra, Daelynn R. Buelow, Shelley Orwick, et al.. (2025). Prognostic, biological, and structural implications of FLT3-JMD point mutations in acute myeloid leukemia: an analysis of Alliance studies. Leukemia. 39(3). 623–631. 1 indexed citations
2.
Lee, Hyunjoo, Shabirul Haque, Jonathan E. Kolitz, et al.. (2024). BCL2 Protein Progressively Declines during Robust CLL Clonal Expansion: Potential Impact on Venetoclax Clinical Efficacy and Insights on Mechanism. PubMed. 2(2). 50–78. 1 indexed citations
3.
Mrózek, Krzysztof, Jessica Kohlschmidt, James S. Blachly, et al.. (2023). Outcome prediction by the 2022 European LeukemiaNet genetic-risk classification for adults with acute myeloid leukemia: an Alliance study. Leukemia. 37(4). 788–798. 31 indexed citations
5.
Bhatnagar, Bhavana, Jessica Kohlschmidt, Shelley Orwick, et al.. (2023). Framework of clonal mutations concurrent with WT1 mutations in adults with acute myeloid leukemia: Alliance for Clinical Trials in Oncology study. Blood Advances. 7(16). 4671–4675. 1 indexed citations
6.
Mazzarello, Andrea Nicola, Mark Fitch, Davide Bagnara, et al.. (2023). Characterization of the Intraclonal Complexity of Chronic Lymphocytic Leukemia B Cells: Potential Influences of B-Cell Receptor Crosstalk with Other Stimuli. Cancers. 15(19). 4706–4706. 2 indexed citations
7.
Ferrer, Gerardo, Rukhsana Aslam, Florencia Palacios, et al.. (2021). Myeloid-derived suppressor cell subtypes differentially influence T-cell function, T-helper subset differentiation, and clinical course in CLL. Leukemia. 35(11). 3163–3175. 37 indexed citations
8.
Palacios, Florencia, Xiao‐Jie Yan, Gerardo Ferrer, et al.. (2021). Musashi 2 influences chronic lymphocytic leukemia cell survival and growth making it a potential therapeutic target. Leukemia. 35(4). 1037–1052. 18 indexed citations
9.
Nasim, Mansoor, David J. Chalif, Alexis Demopoulos, et al.. (2019). Primary Low-Grade B-Cell Lymphoma of Skull With Translocation Between Immunoglobulin and Interferon Regulatory Factor 4 Genes. International Journal of Surgical Pathology. 28(3). 330–335. 3 indexed citations
10.
Papaioannou, Dimitrios, Deedra Nicolet, Hatice Gülçin Özer, et al.. (2019). Prognostic and Biologic Relevance of Clinically Applicable Long Noncoding RNA Profiling in Older Patients with Cytogenetically Normal Acute Myeloid Leukemia. Molecular Cancer Therapeutics. 18(8). 1451–1459. 12 indexed citations
14.
Bagnara, Davide, Matthew Kaufman, Carlo Calissano, et al.. (2011). A novel adoptive transfer model of chronic lymphocytic leukemia suggests a key role for T lymphocytes in the disease. Blood. 117(20). 5463–5472. 145 indexed citations
15.
Calissano, Carlo, Rajendra N. Damle, Sonia Marsilio, et al.. (2011). Intraclonal Complexity in Chronic Lymphocytic Leukemia: Fractions Enriched in Recently Born/Divided and Older/Quiescent Cells. Molecular Medicine. 17(11-12). 1374–1382. 129 indexed citations
16.
Jain, Preetesh, Percy Chiu, Rajendra N. Damle, et al.. (2011). Th17 and non-Th17 interleukin-17-expressing cells in chronic lymphocytic leukemia: delineation, distribution, and clinical relevance. Haematologica. 97(4). 599–607. 63 indexed citations
19.
Messmer, Bradley T., Davorka Messmer, Steven L. Allen, et al.. (2005). In vivo measurements document the dynamic cellular kinetics of chronic lymphocytic leukemia B cells. Journal of Clinical Investigation. 115(3). 755–764. 21 indexed citations
20.
Konrad, Michael, George P. Hemstreet, Evan M. Hersh, et al.. (1990). Pharmacokinetics of recombinant interleukin 2 in humans.. PubMed. 50(7). 2009–17. 175 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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