Aaron S. Meyer

1.6k total citations
44 papers, 1000 citations indexed

About

Aaron S. Meyer is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Aaron S. Meyer has authored 44 papers receiving a total of 1000 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 11 papers in Oncology and 8 papers in Immunology. Recurrent topics in Aaron S. Meyer's work include Cellular Mechanics and Interactions (6 papers), Monoclonal and Polyclonal Antibodies Research (6 papers) and Cancer Cells and Metastasis (5 papers). Aaron S. Meyer is often cited by papers focused on Cellular Mechanics and Interactions (6 papers), Monoclonal and Polyclonal Antibodies Research (6 papers) and Cancer Cells and Metastasis (5 papers). Aaron S. Meyer collaborates with scholars based in United States, France and Canada. Aaron S. Meyer's co-authors include Douglas A. Lauffenburger, Frank B. Gertler, Miles A. Miller, Linda G. Griffith, Annelien J.M. Zweemer, Edward J. Richards, Alan Wells, Shannon K. Hughes, Laura M. Heiser and Irene Bosch and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Aaron S. Meyer

43 papers receiving 991 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aaron S. Meyer United States 17 445 263 247 164 156 44 1000
Steve Bagley United Kingdom 19 663 1.5× 336 1.3× 161 0.7× 236 1.4× 173 1.1× 23 1.3k
Ruth M. Risueño Spain 16 797 1.8× 204 0.8× 492 2.0× 110 0.7× 78 0.5× 36 1.4k
Min Qian China 18 620 1.4× 179 0.7× 181 0.7× 110 0.7× 151 1.0× 35 1.1k
Yevgeniy Romin United States 14 627 1.4× 151 0.6× 134 0.5× 179 1.1× 243 1.6× 27 1.1k
Hadassah Sade Germany 15 607 1.4× 331 1.3× 429 1.7× 89 0.5× 75 0.5× 32 1.4k
Tonny Lagerweij Netherlands 20 456 1.0× 209 0.8× 142 0.6× 55 0.3× 112 0.7× 33 981
Eishu Hirata Japan 17 690 1.6× 441 1.7× 195 0.8× 273 1.7× 206 1.3× 37 1.4k
Andrew J. Loza United States 11 378 0.8× 289 1.1× 209 0.8× 280 1.7× 147 0.9× 22 980
Jun Nakayama Japan 19 752 1.7× 250 1.0× 249 1.0× 88 0.5× 81 0.5× 51 1.1k
Weiyang Jin China 12 320 0.7× 219 0.8× 318 1.3× 254 1.5× 149 1.0× 19 922

Countries citing papers authored by Aaron S. Meyer

Since Specialization
Citations

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

Fields of papers citing papers by Aaron S. Meyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aaron S. Meyer. 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 Aaron S. Meyer. The network helps show where Aaron S. Meyer may publish in the future.

Co-authorship network of co-authors of Aaron S. Meyer

This figure shows the co-authorship network connecting the top 25 collaborators of Aaron S. Meyer. A scholar is included among the top collaborators of Aaron S. Meyer 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 Aaron S. Meyer. Aaron S. Meyer 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.
Millstein, Joshua, Joanne Xiu, Shivani Soni, et al.. (2024). Clinical and molecular characterization of AXL in colorectal cancer: CALGB (Alliance)/SWOG 80405 and real-world data.. Journal of Clinical Oncology. 42(16_suppl). 3145–3145. 1 indexed citations
2.
Kojima, Hidenobu, Thomas A. Morinelli, Yue Wang, et al.. (2024). Group 1 innate lymphoid cells protect liver transplants from ischemia-reperfusion injury via an interferon gamma–mediated pathway. American Journal of Transplantation. 25(5). 969–984.
3.
Meyer, Aaron S., et al.. (2023). Multivalent, asymmetric IL-2–Fc fusions show enhanced selectivity for regulatory T cells. Science Signaling. 16(807). eadg0699–eadg0699. 1 indexed citations
4.
Kojima, Hidenobu, Rebecca A. Sosa, Fady M. Kaldas, et al.. (2023). Disulfide-HMGB1 signals through TLR4 and TLR9 to induce inflammatory macrophages capable of innate-adaptive crosstalk in human liver transplantation. American Journal of Transplantation. 23(12). 1858–1871. 12 indexed citations
5.
Chan, Liana C., et al.. (2023). Tensor-based insights into systems immunity and infectious disease. Trends in Immunology. 44(5). 329–332. 5 indexed citations
6.
Lefaudeux, Diane, et al.. (2023). A stimulus‐contingent positive feedback loop enables IFN‐β dose‐dependent activation of pro‐inflammatory genes. Molecular Systems Biology. 19(5). e11294–e11294. 9 indexed citations
7.
Kulkarni, Prakash, H Wiley, Herbert Levine, et al.. (2023). Addressing the genetic/nongenetic duality in cancer with systems biology. Trends in cancer. 9(3). 185–187. 2 indexed citations
8.
Peyton, Shelly R., Lesley W. Chow, Stacey D. Finley, et al.. (2023). Synthetic living materials in cancer biology. Nature Reviews Bioengineering. 1(12). 972–988. 12 indexed citations
9.
Gross, Sean M., et al.. (2023). Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects. Nature Communications. 14(1). 3450–3450. 25 indexed citations
10.
Lux, Anja, et al.. (2023). Mixed IgG Fc immune complexes exhibit blended binding profiles and refine FcR affinity estimates. Cell Reports. 42(7). 112734–112734. 11 indexed citations
11.
Gross, Sean M., et al.. (2022). A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity. Communications Biology. 5(1). 1258–1258. 8 indexed citations
12.
Meyer, Aaron S., et al.. (2021). A quantitative view of strategies to engineer cell-selective ligand binding. Integrative Biology. 13(11). 269–282. 5 indexed citations
13.
Meyer, Aaron S., et al.. (2021). Tensor‐structured decomposition improves systems serology analysis. Molecular Systems Biology. 17(9). e10243–e10243. 11 indexed citations
14.
Lee, Chang‐Han, Tae Hyun Kang, Ophélie Godon, et al.. (2019). An engineered human Fc domain that behaves like a pH-toggle switch for ultra-long circulation persistence. Nature Communications. 10(1). 5031–5031. 54 indexed citations
15.
Atta, Lyla, et al.. (2018). Systems Modeling Identifies Divergent Receptor Tyrosine Kinase Reprogramming to MAPK Pathway Inhibition. Cellular and Molecular Bioengineering. 11(6). 451–469. 8 indexed citations
16.
Chua, Bernadette A., et al.. (2018). Versatile targeting system for lentiviral vectors involving biotinylated targeting molecules. Virology. 525. 170–181. 9 indexed citations
17.
Miller, Miles A., Madeleine J. Oudin, Ryan J. Sullivan, et al.. (2016). Reduced Proteolytic Shedding of Receptor Tyrosine Kinases Is a Post-Translational Mechanism of Kinase Inhibitor Resistance. Cancer Discovery. 6(4). 382–399. 138 indexed citations
18.
Richards, Edward J., et al.. (2016). JNK Pathway Activation Modulates Acquired Resistance to EGFR/HER2–Targeted Therapies. Cancer Research. 76(18). 5219–5228. 16 indexed citations
19.
Miller, Miles A., Marcia L. Moss, Gary K. Powell, et al.. (2015). Targeting autocrine HB-EGF signaling with specific ADAM12 inhibition using recombinant ADAM12 prodomain. Scientific Reports. 5(1). 15150–15150. 25 indexed citations
20.
Miller, Miles A., Aaron S. Meyer, Michael T. Beste, et al.. (2013). ADAM-10 and -17 regulate endometriotic cell migration via concerted ligand and receptor shedding feedback on kinase signaling. Proceedings of the National Academy of Sciences. 110(22). E2074–83. 75 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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