Jonathan D. Wasserman

6.9k total citations
65 papers, 2.8k citations indexed

About

Jonathan D. Wasserman is a scholar working on Surgery, Endocrinology, Diabetes and Metabolism and Molecular Biology. According to data from OpenAlex, Jonathan D. Wasserman has authored 65 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Surgery, 22 papers in Endocrinology, Diabetes and Metabolism and 17 papers in Molecular Biology. Recurrent topics in Jonathan D. Wasserman's work include Thyroid Cancer Diagnosis and Treatment (16 papers), Thyroid and Parathyroid Surgery (9 papers) and Cancer, Hypoxia, and Metabolism (8 papers). Jonathan D. Wasserman is often cited by papers focused on Thyroid Cancer Diagnosis and Treatment (16 papers), Thyroid and Parathyroid Surgery (9 papers) and Cancer, Hypoxia, and Metabolism (8 papers). Jonathan D. Wasserman collaborates with scholars based in Canada, United States and United Kingdom. Jonathan D. Wasserman's co-authors include Matthew Freeman, David Malkin, María Domínguez, Felix Rintelen, Ernst Hafen, Thomas Radimerski, Hugo Stocker, Martin A. Jünger, Michael E. Greenberg and Harriet Druker and has published in prestigious journals such as Cell, Journal of Biological Chemistry and Journal of Clinical Oncology.

In The Last Decade

Jonathan D. Wasserman

59 papers receiving 2.8k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Jonathan D. Wasserman 1.3k 623 551 504 451 65 2.8k
Monique Losekoot 1.4k 1.1× 300 0.5× 762 1.4× 1.3k 2.5× 283 0.6× 125 3.1k
Gabriel E. DiMattia 1.5k 1.1× 265 0.4× 833 1.5× 795 1.6× 198 0.4× 69 3.5k
Roger G. Clerc 2.2k 1.7× 267 0.4× 238 0.4× 518 1.0× 156 0.3× 21 3.0k
Lena Ho 3.7k 2.9× 628 1.0× 115 0.2× 402 0.8× 432 1.0× 37 5.0k
Rainer Lehtonen 907 0.7× 444 0.7× 414 0.8× 364 0.7× 52 0.1× 69 2.3k
A. Lee Burns 1.4k 1.1× 322 0.5× 308 0.6× 396 0.8× 129 0.3× 55 3.5k
Arthur Gutierrez‐Hartmann 1.8k 1.4× 175 0.3× 723 1.3× 421 0.8× 120 0.3× 79 2.8k
Bruce E. Hayward 1.7k 1.4× 277 0.4× 384 0.7× 1.3k 2.6× 119 0.3× 54 2.9k
Carol A. Wise 2.7k 2.2× 804 1.3× 84 0.2× 1.2k 2.5× 734 1.6× 85 5.7k
E. D. Williams 748 0.6× 363 0.6× 1.0k 1.9× 320 0.6× 86 0.2× 64 2.3k

Countries citing papers authored by Jonathan D. Wasserman

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan D. Wasserman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan D. Wasserman. 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 Jonathan D. Wasserman. The network helps show where Jonathan D. Wasserman may publish in the future.

Co-authorship network of co-authors of Jonathan D. Wasserman

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan D. Wasserman. A scholar is included among the top collaborators of Jonathan D. Wasserman 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 Jonathan D. Wasserman. Jonathan D. Wasserman 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.
Rednam, Surya P., Junne Kamihara, Garrett M. Brodeur, et al.. (2025). Update on Tumor Surveillance for Children with Hereditary Pheochromocytoma/Paraganglioma Syndromes. Clinical Cancer Research. 31(16). 3368–3376. 1 indexed citations
2.
Rednam, Surya P., Anita Villani, Garrett M. Brodeur, et al.. (2025). Update on Surveillance in Von Hippel–Lindau Disease. Clinical Cancer Research. 31(12). 2271–2277. 1 indexed citations
3.
Guo, Eddie, Risa Shorr, Lisa Caulley, et al.. (2025). Representation and Bias in Artificial Intelligence Models for Thyroid Cancer: A Systematic Review. Thyroid. 35(12). 1391–1402.
4.
Michaeli, Orli, Marjolijn C.J. Jongmans, Jonathan D. Wasserman, et al.. (2024). Update on Cancer Screening in Children with Syndromes of Bone Lesions, Hereditary Leiomyomatosis and Renal Cell Carcinoma Syndrome, and Other Rare Syndromes. Clinical Cancer Research. 31(3). 457–465. 4 indexed citations
5.
Shuldiner, Jennifer, Emily Lam, Ann Marie Corrado, et al.. (2024). The decision to disclose to your child they are a childhood cancer survivor: a qualitative study of barriers and facilitators using the theoretical domain framework. Journal of Cancer Survivorship. 1 indexed citations
6.
7.
Ricarte‐Filho, Julio C., Victoria Casado‐Medrano, Erin R. Reichenberger, et al.. (2023). DICER1 RNase IIIb domain mutations trigger widespread miRNA dysregulation and MAPK activation in pediatric thyroid cancer. Frontiers in Endocrinology. 14. 1083382–1083382. 24 indexed citations
8.
Bauer, Andrew J., Jonathan D. Wasserman, & Steven G. Waguespack. (2022). Pediatric thyroid cancer guidelines: challenges in stratifying care based on limited data. European Thyroid Journal. 11(6). 2 indexed citations
9.
Rodríguez‐Galindo, Carlos, Mark Krailo, Emília M. Pinto, et al.. (2021). Treatment of Pediatric Adrenocortical Carcinoma With Surgery, Retroperitoneal Lymph Node Dissection, and Chemotherapy: The Children's Oncology Group ARAR0332 Protocol. Journal of Clinical Oncology. 39(22). 2463–2473. 47 indexed citations
10.
Kaay, Daniëlle C M van der, et al.. (2021). Predicting Malignancy in Pediatric Thyroid Nodules: Early Experience With Machine Learning for Clinical Decision Support. The Journal of Clinical Endocrinology & Metabolism. 106(12). e5236–e5246. 11 indexed citations
11.
Sepiashvili, Lusia, et al.. (2021). Routine T4 No More? Reducing Excess Thyroid Hormone Testing at a Pediatric Tertiary Care Hospital. The Journal of Pediatrics. 236. 269–275.e1. 4 indexed citations
12.
13.
Rednam, Surya P., Ayelet Erez, Harriet Druker, et al.. (2017). Von Hippel–Lindau and Hereditary Pheochromocytoma/Paraganglioma Syndromes: Clinical Features, Genetics, and Surveillance Recommendations in Childhood. Clinical Cancer Research. 23(12). e68–e75. 174 indexed citations
14.
Abu‐Khudir, Rasha, et al.. (2017). Disorders of thyroid morphogenesis. Best Practice & Research Clinical Endocrinology & Metabolism. 31(2). 143–159. 31 indexed citations
15.
Wasserman, Jonathan D., et al.. (2017). Up Schmidt’s creek: When the right treatment goes wrong. Paediatrics & Child Health. 22(4). 175–176.
16.
Villani, Anita, Jonathan D. Wasserman, Derek Stephens, et al.. (2016). Biochemical and imaging surveillance in germline TP53 mutation carriers with Li-Fraumeni syndrome: 11 year follow-up of a prospective observational study. The Lancet Oncology. 17(9). 1295–1305. 322 indexed citations
17.
Vali, Reza, Marianna Rachmiel, Jill Hamilton, et al.. (2014). The role of ultrasound in the follow-up of children with differentiated thyroid cancer. Pediatric Radiology. 45(7). 1039–1045. 10 indexed citations
18.
Wasserman, Jonathan D. & Matthew Freeman. (1998). An Autoregulatory Cascade of EGF Receptor Signaling Patterns the Drosophila Egg. Cell. 95(3). 355–364. 214 indexed citations
19.
Domínguez, María, Jonathan D. Wasserman, & Matthew Freeman. (1998). Multiple functions of the EGF receptor in Drosophila eye development. Current Biology. 8(19). 1039–1048. 184 indexed citations
20.
Howes, Robert I., Jonathan D. Wasserman, & Matthew Freeman. (1998). In Vivo Analysis of Argos Structure-Function. Journal of Biological Chemistry. 273(7). 4275–4281. 27 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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