Ian M. Kaplan

3.1k total citations · 2 hit papers
16 papers, 1.2k citations indexed

About

Ian M. Kaplan is a scholar working on Hematology, Molecular Biology and Oncology. According to data from OpenAlex, Ian M. Kaplan has authored 16 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Hematology, 6 papers in Molecular Biology and 6 papers in Oncology. Recurrent topics in Ian M. Kaplan's work include Acute Myeloid Leukemia Research (4 papers), Chronic Myeloid Leukemia Treatments (4 papers) and CAR-T cell therapy research (3 papers). Ian M. Kaplan is often cited by papers focused on Acute Myeloid Leukemia Research (4 papers), Chronic Myeloid Leukemia Treatments (4 papers) and CAR-T cell therapy research (3 papers). Ian M. Kaplan collaborates with scholars based in United States, France and Germany. Ian M. Kaplan's co-authors include Steven F. Dowdy, Jehangir S. Wadia, Curt I. Civin, Sebastien Morisot, Richard Hildreth, Xiaobing Yu, Jonathan K. Alder, Robert W. Georgantas, Michael A. McDevitt and Jillian Thompson and has published in prestigious journals such as Blood, The Journal of Immunology and PLoS ONE.

In The Last Decade

Ian M. Kaplan

16 papers receiving 1.2k citations

Hit Papers

Cationic TAT peptide transduction domain enters cells by ... 2004 2026 2011 2018 2004 2018 100 200 300 400 500
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.

  1. Cationic TAT peptide transduction domain enters cells by macropinocytosis
    2004 560 citations Ian M. Kaplan, Jehangir S. Wadia et al. Journal of Controlled Release profile →
  2. Pembrolizumab in patients with thymic carcinoma: a single-arm, single-centre, phase 2 study
    2018 277 citations Giuseppe Giaccone, Chul Kim et al. The Lancet Oncology profile →

Peers

Ian M. Kaplan
Tarek Shalaby Switzerland
Daniela Segat Italy
Daniela Di Paolo Italy
LH Shapiro United States
Kunhong Zhong China
Jen‐Chieh Tseng United States
Darren G. Woodside United States
Harris S. Soifer United States
Xiao-Qi Wang United States
Ali C. Ravanpay United States
Tarek Shalaby Switzerland
Ian M. Kaplan
Citations per year, relative to Ian M. Kaplan Ian M. Kaplan (= 1×) peers Tarek Shalaby

Countries citing papers authored by Ian M. Kaplan

Since Specialization
Citations

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

Fields of papers citing papers by Ian M. Kaplan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ian M. Kaplan. 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 Ian M. Kaplan. The network helps show where Ian M. Kaplan may publish in the future.

Co-authorship network of co-authors of Ian M. Kaplan

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

All Works

16 of 16 papers shown
1.
Lee, Lik Wee, Shahin Shafiani, Beryl Crossley, et al.. (2022). Characterisation of T cell receptor repertoires in coeliac disease. Journal of Clinical Pathology. 77(2). 116–124. 3 indexed citations
2.
Carty, Cara L., Namrata Singh, Lionel Le Bourhis, et al.. (2022). DOP47 Identification and characterization of T-cell receptor sequences associated with Crohn’s Disease. Journal of Crohn s and Colitis. 16(Supplement_1). i096–i097. 1 indexed citations
3.
Meskini, Rajaâ El, Nathan Pate, Michael D. Oberst, et al.. (2021). Distinct Biomarker Profiles and TCR Sequence Diversity Characterize the Response to PD-L1 Blockade in a Mouse Melanoma Model. Molecular Cancer Research. 19(8). 1422–1436. 3 indexed citations
4.
Giaccone, Giuseppe, Chul Kim, Jillian Thompson, et al.. (2018). Pembrolizumab in patients with thymic carcinoma: a single-arm, single-centre, phase 2 study. The Lancet Oncology. 19(3). 347–355. 277 indexed citations breakdown →
5.
Tran, Phuoc T., Ryan Phillips, Noura Radwan, et al.. (2018). SABR Produces Systemic Adaptive Immune Responses in Castration-Sensitive Oligometastatic Prostate Cancer Patients. International Journal of Radiation Oncology*Biology*Physics. 102(3). S24–S25. 2 indexed citations
6.
Heiser, Diane, Yee Sun Tan, Ian M. Kaplan, et al.. (2014). Correlated miR-mRNA Expression Signatures of Mouse Hematopoietic Stem and Progenitor Cell Subsets Predict “Stemness” and “Myeloid” Interaction Networks. PLoS ONE. 9(4). e94852–e94852. 6 indexed citations
7.
Chu, S. Haihua, Li Li, Ian M. Kaplan, et al.. (2012). FLT3-ITD Knockin Impairs Hematopoietic Stem Cell Quiescence/Homeostasis, Leading to Myeloproliferative Neoplasm. Cell stem cell. 11(3). 346–358. 65 indexed citations
8.
Park, Tea Soon, Jeffrey S. Huo, Ann Peters, et al.. (2012). Growth Factor-Activated Stem Cell Circuits and Stromal Signals Cooperatively Accelerate Non-Integrated iPSC Reprogramming of Human Myeloid Progenitors. PLoS ONE. 7(8). e42838–e42838. 25 indexed citations
9.
Chu, S. Haihua, et al.. (2011). FLT3-ITD Knock-in Impairs Hematopoietic Stem Cell Quiescence/Homeostasis, Leading to a Myeloproliferative Neoplasm. Blood. 118(21). 46–46. 5 indexed citations
10.
Kaplan, Ian M., Sebastien Morisot, Diane Heiser, et al.. (2011). Deletion of Tristetraprolin Caused Spontaneous Reactive Granulopoiesis by a Non–Cell-Autonomous Mechanism Without Disturbing Long-Term Hematopoietic Stem Cell Quiescence. The Journal of Immunology. 186(5). 2826–2834. 23 indexed citations
11.
Morisot, Sebastien, Alan S. Wayne, Osnat Bohana‐Kashtan, et al.. (2010). High frequencies of leukemia stem cells in poor-outcome childhood precursor-B acute lymphoblastic leukemias. Leukemia. 24(11). 1859–1866. 37 indexed citations
12.
13.
Alder, Jonathan K., Robert W. Georgantas, Richard Hildreth, et al.. (2008). Kruppel-Like Factor 4 Is Essential for Inflammatory Monocyte Differentiation In Vivo. The Journal of Immunology. 180(8). 5645–5652. 169 indexed citations
14.
Morisot, Sebastien, Alan S. Wayne, Osnat Bohana‐Kashtan, et al.. (2008). Leukemia Stem Cells (LSCs) Are Frequent in Childhood Precursor B Acute Lymphoblastic Leukemia (ALL).. Blood. 112(11). 1354–1354. 3 indexed citations
15.
Snyder, Eric L., Cheryl Saenz, Catherine Denicourt, et al.. (2005). Enhanced Targeting and Killing of Tumor Cells Expressing the CXC Chemokine Receptor 4 by Transducible Anticancer Peptides. Cancer Research. 65(23). 10646–10650. 66 indexed citations
16.
Kaplan, Ian M., Jehangir S. Wadia, & Steven F. Dowdy. (2004). Cationic TAT peptide transduction domain enters cells by macropinocytosis. Journal of Controlled Release. 102(1). 247–253. 560 indexed citations breakdown →

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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