Daniel T. Starczynowski

11.6k total citations · 1 hit paper
89 papers, 3.4k citations indexed

About

Daniel T. Starczynowski is a scholar working on Hematology, Molecular Biology and Immunology. According to data from OpenAlex, Daniel T. Starczynowski has authored 89 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Hematology, 50 papers in Molecular Biology and 41 papers in Immunology. Recurrent topics in Daniel T. Starczynowski's work include Acute Myeloid Leukemia Research (47 papers), NF-κB Signaling Pathways (24 papers) and Immune Response and Inflammation (15 papers). Daniel T. Starczynowski is often cited by papers focused on Acute Myeloid Leukemia Research (47 papers), NF-κB Signaling Pathways (24 papers) and Immune Response and Inflammation (15 papers). Daniel T. Starczynowski collaborates with scholars based in United States, Canada and Japan. Daniel T. Starczynowski's co-authors include Garrett W. Rhyasen, Aly Karsan, Thomas D. Gilmore, Wan L. Lam, Timothy M. Chlon, Laura Barreyro, Jennifer J. Trowbridge, Demetrios Kalaitzidis, R. Keith Humphries and Florian Kuchenbauer and has published in prestigious journals such as Nature, Journal of Clinical Investigation and Nature Medicine.

In The Last Decade

Daniel T. Starczynowski

82 papers receiving 3.4k citations

Hit Papers

Identification of miR-145 and miR-146a as mediators of th... 2009 2026 2014 2020 2009 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel T. Starczynowski United States 32 1.8k 1.4k 1.3k 1.1k 451 89 3.4k
Jan Jacob Schuringa Netherlands 39 2.1k 1.1× 1.8k 1.3× 918 0.7× 570 0.5× 581 1.3× 147 4.1k
Jianguo Tao United States 25 1.8k 1.0× 564 0.4× 617 0.5× 680 0.6× 347 0.8× 76 2.8k
Renate Burger Germany 27 1.6k 0.9× 990 0.7× 628 0.5× 569 0.5× 221 0.5× 66 2.9k
Shaohua Chen China 26 1.0k 0.6× 536 0.4× 1.4k 1.1× 576 0.5× 216 0.5× 184 3.0k
Tushar D. Bhagat United States 24 1.5k 0.8× 410 0.3× 438 0.3× 689 0.6× 210 0.5× 55 2.4k
Roberta Riccioni Italy 23 1.4k 0.7× 812 0.6× 461 0.4× 302 0.3× 234 0.5× 45 2.1k
Goro Sashida Japan 28 2.0k 1.1× 1.1k 0.7× 287 0.2× 267 0.2× 404 0.9× 88 2.7k
Shiang Huang China 29 1.3k 0.7× 402 0.3× 809 0.6× 471 0.4× 232 0.5× 86 2.6k
Martina Seiffert Germany 32 1.2k 0.7× 340 0.2× 1.5k 1.2× 542 0.5× 900 2.0× 80 3.1k

Countries citing papers authored by Daniel T. Starczynowski

Since Specialization
Citations

This map shows the geographic impact of Daniel T. Starczynowski'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 Daniel T. Starczynowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel T. Starczynowski more than expected).

Fields of papers citing papers by Daniel T. Starczynowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel T. Starczynowski. 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 Daniel T. Starczynowski. The network helps show where Daniel T. Starczynowski may publish in the future.

Co-authorship network of co-authors of Daniel T. Starczynowski

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel T. Starczynowski. A scholar is included among the top collaborators of Daniel T. Starczynowski 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 Daniel T. Starczynowski. Daniel T. Starczynowski 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.
Choi, Issac, Courtnee Clough, Aishlin Hassan, et al.. (2026). Scaffolding-dependent CASP1 constrains excessive cell-intrinsic inflammatory signaling in leukemia. Cell chemical biology. 33(1). 59–73.e10.
2.
Hassan, Aishlin, Kwangmin Choi, Courtnee Clough, et al.. (2025). Targeting of IRAK4 and GSPT1 enhances therapeutic efficacy in AML via c-Myc destabilization. Leukemia. 39(9). 2163–2173. 2 indexed citations
4.
Barreyro, Laura, Kathleen Hueneman, Issac Choi, et al.. (2025). Ubiquitin-conjugating enzyme UBE2N modulates proteostasis in immunoproteasome-positive acute myeloid leukemia. Journal of Clinical Investigation. 135(10). 4 indexed citations
5.
Hassan, Aishlin, Kathleen Hueneman, Averil Ma, et al.. (2024). Chemotherapy resistance in acute myeloid leukemia is mediated by A20 suppression of spontaneous necroptosis. Nature Communications. 15(1). 9189–9189. 8 indexed citations
6.
Bolanos, Lyndsey, Kwangmin Choi, Shokichi Tsukamoto, et al.. (2024). Metabolic reprogramming regulated by TRAF6 contributes to the leukemia progression. Leukemia. 38(5). 1032–1045. 3 indexed citations
8.
Zhu, Xiaoqin, Michael A. Wyder, Nathan Salomonis, et al.. (2023). Repression of TRIM13 by chromatin assembly factor CHAF1B is critical for AML development. Blood Advances. 7(17). 4822–4837. 4 indexed citations
9.
Agarwal, Puneet, Jennifer Yeung, Lyndsey Bolanos, et al.. (2023). Paralog-specific signaling by IRAK1/4 maintains MyD88-independent functions in MDS/AML. Blood. 142(11). 989–1007. 27 indexed citations
10.
Muto, Tomoya, Puneet Agarwal, Kwangmin Choi, et al.. (2023). Inactivation of p53 provides a competitive advantage to del(5q) myelodysplastic syndrome hematopoietic stem cells during inflammation. Haematologica. 108(10). 2715–2729. 4 indexed citations
11.
LeBlanc, Francis, et al.. (2023). Targeting CREB-Binding Protein (CREBBP) Overcomes Resistance to Azacitidine and Venetoclax Therapy in Acute Myeloid Leukemia (AML). Blood. 142(Supplement 1). 5765–5765. 1 indexed citations
12.
Hueneman, Kathleen, Lyndsey Bolanos, Kwangmin Choi, et al.. (2021). The deubiquitinase USP15 modulates cellular redox and is a therapeutic target in acute myeloid leukemia. Leukemia. 36(2). 438–451. 25 indexed citations
13.
Azhar, Mohammad, Zachary Kincaid, Meenu Kesarwani, et al.. (2021). Momelotinib is a highly potent inhibitor of FLT3-mutant AML. Blood Advances. 6(4). 1186–1192. 15 indexed citations
14.
Trowbridge, Jennifer J. & Daniel T. Starczynowski. (2021). Innate immune pathways and inflammation in hematopoietic aging, clonal hematopoiesis, and MDS. The Journal of Experimental Medicine. 218(7). 114 indexed citations
15.
Serrano‐López, Juana, Shailaja Hegde, Sachin Kumar, et al.. (2021). Inflammation rapidly recruits mammalian GMP and MDP from bone marrow into regional lymphatics. eLife. 10. 4 indexed citations
16.
Chlon, Timothy M., Courtney E. Hershberger, Kathleen Hueneman, et al.. (2021). Germline DDX41 mutations cause ineffective hematopoiesis and myelodysplasia. Cell stem cell. 28(11). 1966–1981.e6. 57 indexed citations
18.
Fang, Jing, Lyndsey Bolanos, Xiaona Liu, et al.. (2014). Myeloid Malignancies with Chromosome 5q Deletions Acquire a Dependency on an Intrachromosomal NF-κB Gene Network. Cell Reports. 8(5). 1328–1338. 49 indexed citations
19.
Starczynowski, Daniel T., Ryan D. Morin, Andrew McPherson, et al.. (2010). Genome-wide identification of human microRNAs located in leukemia-associated genomic alterations. Blood. 117(2). 595–607. 94 indexed citations
20.
Kalaitzidis, Demetrios, et al.. (2004). Characterization of a human REL-estrogen receptor fusion protein with a reverse conditional transforming activity in chicken spleen cells. Oncogene. 23(45). 7580–7587. 9 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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