Kim Rosenthal

951 total citations
10 papers, 331 citations indexed

About

Kim Rosenthal is a scholar working on Molecular Biology, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Kim Rosenthal has authored 10 papers receiving a total of 331 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Oncology and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Kim Rosenthal's work include Monoclonal and Polyclonal Antibodies Research (4 papers), HER2/EGFR in Cancer Research (3 papers) and Glycosylation and Glycoproteins Research (2 papers). Kim Rosenthal is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (4 papers), HER2/EGFR in Cancer Research (3 papers) and Glycosylation and Glycoproteins Research (2 papers). Kim Rosenthal collaborates with scholars based in United States, Germany and Singapore. Kim Rosenthal's co-authors include Herren Wu, William F. Dall’Acqua, Melissa Damschroder, Kris F. Sachsenmeier, James C. Geoghegan, Lori Clarke, Xiaojun Lu, Gundo Diedrich, Daniel C. Rowe and Yue Wang and has published in prestigious journals such as Journal of Biological Chemistry, PLoS ONE and Scientific Reports.

In The Last Decade

Kim Rosenthal

10 papers receiving 320 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kim Rosenthal United States 8 148 128 113 87 71 10 331
Angelo Corso Faini Italy 10 96 0.6× 124 1.0× 95 0.8× 23 0.3× 127 1.8× 20 328
Sylvia Gruber Austria 12 67 0.5× 110 0.9× 80 0.7× 77 0.9× 39 0.5× 27 369
Erika M. Cook United States 9 150 1.0× 106 0.8× 224 2.0× 172 2.0× 7 0.1× 18 433
Mau‐Shin Chi Taiwan 9 117 0.8× 62 0.5× 33 0.3× 36 0.4× 20 0.3× 17 327
Tyler R. McCaw United States 9 153 1.0× 125 1.0× 82 0.7× 20 0.2× 6 0.1× 18 317
F R Agbanyo Canada 9 75 0.5× 30 0.2× 68 0.6× 13 0.1× 41 0.6× 12 340
Surya Kumari Vadrevu United States 7 176 1.2× 161 1.3× 324 2.9× 29 0.3× 5 0.1× 11 481
Danice Wilkins United States 10 120 0.8× 177 1.4× 345 3.1× 24 0.3× 7 0.1× 14 518
Nicholas A. Zorko United States 11 104 0.7× 165 1.3× 176 1.6× 28 0.3× 5 0.1× 31 470
Serena Zanotta Italy 13 86 0.6× 203 1.6× 197 1.7× 31 0.4× 9 0.1× 22 393

Countries citing papers authored by Kim Rosenthal

Since Specialization
Citations

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

Fields of papers citing papers by Kim Rosenthal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kim Rosenthal. 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 Kim Rosenthal. The network helps show where Kim Rosenthal may publish in the future.

Co-authorship network of co-authors of Kim Rosenthal

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

All Works

10 of 10 papers shown
1.
Brady, Tyler, Tianhui Zhang, Kevin M. Tuffy, et al.. (2022). Qualification of a Biolayer Interferometry Assay to Support AZD7442 Resistance Monitoring. Microbiology Spectrum. 10(5). e0103422–e0103422. 3 indexed citations
2.
Shan, Lu, Bilal Omar, Adem C. Koksal, et al.. (2020). Long-acting antibody ligand mimetics for HER4-selective agonism. Scientific Reports. 10(1). 17257–17257. 1 indexed citations
4.
Sun, Junhui, Weidong Hao, Natasha Fillmore, et al.. (2019). Human Relaxin‐2 Fusion Protein Treatment Prevents and Reverses Isoproterenol‐Induced Hypertrophy and Fibrosis in Mouse Heart. Journal of the American Heart Association. 8(24). e013465–e013465. 14 indexed citations
5.
Oganesyan, Vaheh, Peng Li, Jared S. Bee, et al.. (2018). Structural insights into the mechanism of action of a biparatopic anti-HER2 antibody. Journal of Biological Chemistry. 293(22). 8439–8448. 55 indexed citations
6.
Li, Qing, Jason B. White, Norman Peterson, et al.. (2018). Tumor uptake of pegylated diabodies: Balancing systemic clearance and vascular transport. Journal of Controlled Release. 279. 126–135. 19 indexed citations
7.
Geoghegan, James C., Gundo Diedrich, Xiaojun Lu, et al.. (2016). Inhibition of CD73 AMP hydrolysis by a therapeutic antibody with a dual, non-competitive mechanism of action. mAbs. 8(3). 454–467. 84 indexed citations
8.
Chen, Bo, Allison L. Miller, Marlon C. Rebelatto, et al.. (2015). S100A9 Induced Inflammatory Responses Are Mediated by Distinct Damage Associated Molecular Patterns (DAMP) Receptors In Vitro and In Vivo. PLoS ONE. 10(2). e0115828–e0115828. 95 indexed citations
9.
Sachsenmeier, Kris F., Carl Hay, Lori Clarke, et al.. (2012). Development of a Novel Ectonucleotidase Assay Suitable for High-Throughput Screening. SLAS DISCOVERY. 17(7). 993–998. 15 indexed citations
10.
Aggarwal, Sudeepta, Tao He, William W. Fitzhugh, et al.. (2009). Immune modulator CD70 as a potential cisplatin resistance predictive marker in ovarian cancer. Gynecologic Oncology. 115(3). 430–437. 28 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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