Gabriel K. Griffin

4.5k total citations · 5 hit papers
17 papers, 2.0k citations indexed

About

Gabriel K. Griffin is a scholar working on Hematology, Molecular Biology and Genetics. According to data from OpenAlex, Gabriel K. Griffin has authored 17 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Hematology, 8 papers in Molecular Biology and 7 papers in Genetics. Recurrent topics in Gabriel K. Griffin's work include Acute Myeloid Leukemia Research (11 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (7 papers) and Cancer Genomics and Diagnostics (6 papers). Gabriel K. Griffin is often cited by papers focused on Acute Myeloid Leukemia Research (11 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (7 papers) and Cancer Genomics and Diagnostics (6 papers). Gabriel K. Griffin collaborates with scholars based in United States, Sweden and Italy. Gabriel K. Griffin's co-authors include Alexander G. Bick, Aviv Regev, Gökçen Eraslan, Pradeep Natarajan, James P. Pirruccello, Peter Libby, Jonas Frisén, Ludvig Bergenstråhle, Joshua Gould and Tarmo Äijö and has published in prestigious journals such as Cell, Circulation and Nature Medicine.

In The Last Decade

Gabriel K. Griffin

16 papers receiving 2.0k citations

Hit Papers

High-definition spatial transcriptomics for in situ tissu... 2019 2026 2021 2023 2019 2020 2019 2021 2023 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gabriel K. Griffin United States 10 1.0k 556 406 357 333 17 2.0k
Vilma Mantovani Italy 29 721 0.7× 132 0.2× 263 0.6× 265 0.7× 279 0.8× 87 2.0k
Merrilee Needham Australia 29 859 0.8× 70 0.1× 410 1.0× 298 0.8× 136 0.4× 112 2.5k
Linda Fredriksson Sweden 19 850 0.8× 253 0.5× 158 0.4× 218 0.6× 433 1.3× 25 2.1k
Siddhartha Kar United Kingdom 25 587 0.6× 197 0.4× 300 0.7× 136 0.4× 332 1.0× 53 1.8k
Attila J. Fabian United States 15 521 0.5× 303 0.5× 219 0.5× 271 0.8× 60 0.2× 16 1.8k
Stephan M. Tanner United States 23 1.4k 1.4× 539 1.0× 279 0.7× 101 0.3× 290 0.9× 44 2.4k
Lucia Centurione Italy 20 551 0.5× 176 0.3× 233 0.6× 252 0.7× 131 0.4× 50 1.2k
Vesa Ruotsalainen Finland 22 2.1k 2.0× 120 0.2× 275 0.7× 557 1.6× 46 0.1× 26 4.4k
Georgina Caruana Australia 26 1.2k 1.2× 85 0.2× 91 0.2× 245 0.7× 104 0.3× 42 2.0k
Christoph Renné Germany 20 508 0.5× 314 0.6× 472 1.2× 738 2.1× 136 0.4× 29 2.1k

Countries citing papers authored by Gabriel K. Griffin

Since Specialization
Citations

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

Fields of papers citing papers by Gabriel K. Griffin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriel K. Griffin. 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 Gabriel K. Griffin. The network helps show where Gabriel K. Griffin may publish in the future.

Co-authorship network of co-authors of Gabriel K. Griffin

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

All Works

17 of 17 papers shown
1.
Benfatto, Salvatore, Jonathan H. Young, Phillip Michaels, et al.. (2025). Rapid epigenomic classification of acute leukemia. Nature Genetics. 57(10). 2456–2467. 1 indexed citations
2.
Äijö, Tarmo, Britta Lötstedt, Nemanja D. Marjanovic, et al.. (2025). Tissue and cellular spatiotemporal dynamics in colon aging. Nature Biotechnology.
3.
Benfatto, Salvatore, Phillip Michaels, Christoph Schliemann, et al.. (2024). Rapid Epigenomic Classification of Acute Leukemia. Blood. 144(Supplement 1). 273–273. 1 indexed citations
4.
Zon, Rebecca L., Aswin Sekar, Katharine Clapham, et al.. (2024). JAK2-mutant clonal hematopoiesis is associated with venous thromboembolism. Blood. 144(20). 2149–2154. 21 indexed citations
5.
Sekar, Aswin, Katharine Clapham, Abhishek Niroula, et al.. (2023). Clonal Hematopoiesis and Venous Thromboembolism in the UK Biobank. Blood. 142(Supplement 1). 568–568. 2 indexed citations
6.
Cutler, Corey, Vincent T. Ho, John Koreth, et al.. (2023). Post-Transplant T Cell Clonotype Diversity Is Associated with Survival in Patients with TP53-Mutated Acute Myeloid Leukemia. Blood. 142(Supplement 1). 2176–2176. 1 indexed citations
7.
Romine, Kyle A., Yoke Seng Lee, Adam S. Sperling, et al.. (2023). DNMT3A-Mutated Stem and Progenitor Cells Contribute to Altered T Cell Activation in Clonal Hematopoiesis. Blood. 142(Supplement 1). 1320–1320. 1 indexed citations
8.
Weeks, Lachelle D., Abhishek Niroula, Donna Neuberg, et al.. (2023). Prediction of Risk for Myeloid Malignancy in Clonal Hematopoiesis. NEJM Evidence. 2(5). 139 indexed citations breakdown →
9.
Weeks, Lachelle D., Abhishek Niroula, Donna Neuberg, et al.. (2022). Prediction of Risk for Myeloid Malignancy in Clonal Hematopoiesis. Blood. 140(Supplement 1). 2229–2231. 21 indexed citations
10.
Niroula, Abhishek, Aswin Sekar, Mark A. Murakami, et al.. (2021). Distinction of lymphoid and myeloid clonal hematopoiesis. Nature Medicine. 27(11). 1921–1927. 161 indexed citations breakdown →
11.
Pasupuleti, Santhosh Kumar, Baskar Ramdas, Sarah S. Burns, et al.. (2021). Obesity-Induced Inflammation Co-Operates with Clonal Hematopoiesis of Indeterminate Potential (CHIP) Mutants to Promote Leukemia Development and Cardiovascular Disease. Blood. 138(Supplement 1). 1094–1094. 7 indexed citations
12.
Bhattacharya, Romit, Seyedeh M. Zekavat, Md Mesbah Uddin, et al.. (2021). Association of Diet Quality With Prevalence of Clonal Hematopoiesis and Adverse Cardiovascular Events. JAMA Cardiology. 6(9). 1069–1069. 66 indexed citations
13.
Honigberg, Michael C., Seyedeh M. Zekavat, Abhishek Niroula, et al.. (2020). Premature Menopause, Clonal Hematopoiesis, and Coronary Artery Disease in Postmenopausal Women. Circulation. 143(5). 410–423. 96 indexed citations
14.
Drokhlyansky, Eugene, Christopher S. Smillie, Nicholas Van Wittenberghe, et al.. (2020). The Human and Mouse Enteric Nervous System at Single-Cell Resolution. Cell. 182(6). 1606–1622.e23. 337 indexed citations breakdown →
15.
Vicković, Sanja, Gökçen Eraslan, Fredrik Salmén, et al.. (2019). High-definition spatial transcriptomics for in situ tissue profiling. Nature Methods. 16(10). 987–990. 705 indexed citations breakdown →
16.
Bick, Alexander G., James P. Pirruccello, Gabriel K. Griffin, et al.. (2019). Genetic Interleukin 6 Signaling Deficiency Attenuates Cardiovascular Risk in Clonal Hematopoiesis. Circulation. 141(2). 124–131. 267 indexed citations breakdown →
17.
Amabile, Nicolas, Susan Cheng, Martin G. Larson, et al.. (2014). Association of circulating endothelial microparticles with cardiometabolic risk factors in the Framingham Heart Study. European Heart Journal. 35(42). 2972–2979. 181 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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