Ryan M. Young

44 papers receiving 10.0k citations

Ryan M. Young's Hit Papers

A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications 2020 · 832 citations
8320+5+10Years since publication50010001.5k

Peers

Ryan M. Young
Comparison fields: 5 of 117
  • Pathology and Forensic Medicine 5.9k
  • Genetics 3.5k
  • Oncology 3.4k
  • Immunology 2.5k
  • Cancer Research 1.2k
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Citations per year

Countries citing papers authored by Ryan M. Young

Since Specialization
Citations

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

Fields of papers citing papers by Ryan M. Young

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ryan M. Young, 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 Ryan M. Young Line = papers co-authored together Ryan M. Young links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Genetics and Pathogenesis of Diffuse Large B-Cell Lymphoma
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20181669
2
Chronic active B-cell-receptor signalling in diffuse large B-cell lymphoma
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20101365
3
Oncogenically active MYD88 mutations in human lymphoma
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20101238
4
Targeting B cell receptor signaling with ibrutinib in diffuse large B cell lymphoma
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2015957
5
A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications
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2020832
6
Burkitt lymphoma pathogenesis and therapeutic targets from structural and functional genomics
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2012707
7 2012435
8 2012364
9 2013327
10 2014324
11 2018317
12 2015179
13 2015156
14 2003144
15 2005122
16 2007116
17 2019107
18 2008105
19 201686
20 200877

About Ryan M. Young

Ryan M. Young is a scholar working on Pathology and Forensic Medicine, Genetics, Immunology, Oncology and Hematology, having authored 47 papers that have together received 10.1k indexed citations. Recurring topics across this work include Lymphoma Diagnosis and Treatment (24 papers), Chronic Lymphocytic Leukemia Research (15 papers), Immune Cell Function and Interaction (15 papers), CAR-T cell therapy research (9 papers), Ubiquitin and proteasome pathways (4 papers), Protein Degradation and Inhibitors (3 papers), Monoclonal and Polyclonal Antibodies Research (3 papers) and Multiple Myeloma Research and Treatments (3 papers). The work is most often cited by research in Pathology and Forensic Medicine (5.9k citations), Genetics (3.5k citations), Oncology (3.4k citations), Immunology (2.5k citations) and Cancer Research (1.2k citations). Ryan M. Young has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Louis M. Staudt, Arthur L. Shaffer, James D. Phelan, David Holowka, Wyndham Hopkins Wilson, Barbara A. Baird, Roland P. H. Schmitz, Da Wei Huang, George W. Wright and Calvin A. Johnson. Their work appears in journals such as Cancer Cell, Blood, Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

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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