Jonathan W. Riess

14.0k total citations · 2 hit papers
164 papers, 4.2k citations indexed

About

Jonathan W. Riess is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, Jonathan W. Riess has authored 164 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 102 papers in Oncology, 100 papers in Pulmonary and Respiratory Medicine and 40 papers in Cancer Research. Recurrent topics in Jonathan W. Riess's work include Lung Cancer Treatments and Mutations (88 papers), Cancer Genomics and Diagnostics (32 papers) and Colorectal Cancer Treatments and Studies (30 papers). Jonathan W. Riess is often cited by papers focused on Lung Cancer Treatments and Mutations (88 papers), Cancer Genomics and Diagnostics (32 papers) and Colorectal Cancer Treatments and Studies (30 papers). Jonathan W. Riess collaborates with scholars based in United States, United Kingdom and Spain. Jonathan W. Riess's co-authors include Heather A. Wakelee, Joel W. Neal, Julie R. Brahmer, Judy Lieberman, Ronald G. Collman, Premlata Shankar, Sang‐Kyung Lee, Michael F. Murray, Derek M. Dykxhoorn and Carl D. Novina and has published in prestigious journals such as Nature Medicine, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Jonathan W. Riess

147 papers receiving 4.2k citations

Hit Papers

siRNA-directed inhibition... 2002 2026 2010 2018 2002 2021 200 400 600

Author Peers

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

Author Last Decade Papers Cites
Jonathan W. Riess 2.4k 2.0k 1.4k 733 444 164 4.2k
Brian A. Van Tine 1.7k 0.7× 1.6k 0.8× 1.3k 0.9× 432 0.6× 399 0.9× 141 3.8k
Shigeki Shimizu 1.4k 0.6× 1.9k 1.0× 1.0k 0.7× 481 0.7× 457 1.0× 108 3.5k
Antonio Chella 2.9k 1.2× 3.5k 1.8× 1.6k 1.1× 1.2k 1.6× 260 0.6× 130 5.5k
Winan J. van Houdt 2.8k 1.2× 1.4k 0.7× 1.6k 1.1× 685 0.9× 482 1.1× 166 5.2k
Samra Turajlic 2.4k 1.0× 1.4k 0.7× 2.3k 1.6× 1.3k 1.8× 821 1.8× 98 4.9k
Faye M. Johnson 2.1k 0.9× 1.0k 0.5× 2.2k 1.5× 629 0.9× 302 0.7× 150 4.4k
Gordana Vlahovic 2.2k 0.9× 956 0.5× 920 0.6× 454 0.6× 973 2.2× 93 3.9k
Paul K. Paik 3.0k 1.3× 3.5k 1.8× 2.2k 1.5× 1.1k 1.5× 471 1.1× 155 5.6k
Joseph Boni 1.8k 0.8× 1.4k 0.7× 2.5k 1.7× 822 1.1× 324 0.7× 88 4.7k
Boe Sandahl Sørensen 2.0k 0.8× 1.3k 0.7× 2.7k 1.9× 1.6k 2.1× 380 0.9× 171 4.9k

Countries citing papers authored by Jonathan W. Riess

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan W. Riess

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonathan W. Riess

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan W. Riess. A scholar is included among the top collaborators of Jonathan W. Riess 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 W. Riess. Jonathan W. Riess 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.
Fiehn, Oliver, Lina A. Dahabiyeh, Rubén Fragoso, et al.. (2025). Longitudinal Plasma Lipidomics Reveals Distinct Signatures Following Surgery in Patients with Glioblastoma. Metabolites. 15(10). 673–673.
2.
Fiehn, Oliver, Lina A. Dahabiyeh, Rubén Fragoso, et al.. (2025). TMET-13. Metabolomic and lipidomic profiling reveals distinct subtypes of IDH-wildtype glioblastoma and shared metabolic features with IDH-mutant astrocytoma grade 4. Neuro-Oncology. 27(Supplement_5). v431–v431.
4.
Schram, Alison M., Abdul Rafeh Naqash, Eric B. Haura, et al.. (2025). The Bi-steric, mTORC1-Selective Inhibitor, RMC-5552, in Advanced Solid Tumors: A Phase 1 Trial. Clinical Cancer Research. 31(23). 4933–4943.
7.
Tsao, Anne S., Marianna Koczywas, Jonathan W. Riess, et al.. (2024). S1701: A randomized phase II trial of carboplatin-paclitaxel with and without ramucirumab in patients with locally advanced, recurrent, or metastatic thymic carcinoma.. Journal of Clinical Oncology. 42(16_suppl). 8110–8110. 3 indexed citations
10.
Cornelissen, Robin, Arsela Prelaj, Sophie Sun, et al.. (2023). Poziotinib in Treatment-Naive NSCLC Harboring HER2 Exon 20 Mutations: ZENITH20-4, A Multicenter, Multicohort, Open-Label, Phase 2 Trial (Cohort 4). Journal of Thoracic Oncology. 18(8). 1031–1041. 34 indexed citations
11.
Aboud, Orwa, Yin Liu, Oliver Fiehn, et al.. (2023). Application of Machine Learning to Metabolomic Profile Characterization in Glioblastoma Patients Undergoing Concurrent Chemoradiation. Metabolites. 13(2). 299–299. 13 indexed citations
12.
Gadgeel, Shirish M., Jieling Miao, Jonathan W. Riess, et al.. (2023). Phase II Study of Docetaxel and Trametinib in Patients with KRAS Mutation Positive Recurrent Non–Small Cell Lung Cancer (NSCLC; SWOG S1507, NCT-02642042). Clinical Cancer Research. 29(18). 3641–3649. 9 indexed citations
13.
Kurzrock, Razelle, Charu Aggarwal, Caroline Weipert, et al.. (2022). Prevalence of ARID1A Mutations in Cell-Free Circulating Tumor DNA in a Cohort of 71,301 Patients and Association with Driver Co-Alterations. Cancers. 14(17). 4281–4281. 10 indexed citations
14.
Cheng, Michael L., Christie J. Lau, Marina S.D. Milan, et al.. (2021). Plasma ctDNA Response Is an Early Marker of Treatment Effect in Advanced NSCLC. JCO Precision Oncology. 5(5). 393–402. 20 indexed citations
15.
Rich, Thereasa A., Karen L. Reckamp, Young Kwang Chae, et al.. (2019). Analysis of Cell-Free DNA from 32,989 Advanced Cancers Reveals Novel Co-occurring Activating RET Alterations and Oncogenic Signaling Pathway Aberrations. Clinical Cancer Research. 25(19). 5832–5842. 68 indexed citations
16.
Hellyer, Jessica A., Matthew A. Gubens, Kristen Cunanan, et al.. (2019). Phase II trial of single agent amrubicin in patients with previously treated advanced thymic malignancies. Lung Cancer. 137. 71–75. 11 indexed citations
17.
Floc’h, Nicolas, Matthew J. Martin, Jonathan W. Riess, et al.. (2018). Antitumor Activity of Osimertinib, an Irreversible Mutant-Selective EGFR Tyrosine Kinase Inhibitor, in NSCLC Harboring EGFR Exon 20 Insertions. Molecular Cancer Therapeutics. 17(5). 885–896. 85 indexed citations
18.
Turner, David C., Anna Kondic, Keaven M. Anderson, et al.. (2018). Pembrolizumab Exposure–Response Assessments Challenged by Association of Cancer Cachexia and Catabolic Clearance. Clinical Cancer Research. 24(23). 5841–5849. 156 indexed citations
19.
Neel, Dana S., Victor Olivas, Manasi K. Mayekar, et al.. (2018). Differential Subcellular Localization Regulates Oncogenic Signaling by ROS1 Kinase Fusion Proteins. Cancer Research. 79(3). 546–556. 63 indexed citations
20.
Jahchan, Nadine S., Joel T. Dudley, Paweł K. Mazur, et al.. (2013). A Drug Repositioning Approach Identifies Tricyclic Antidepressants as Inhibitors of Small Cell Lung Cancer and Other Neuroendocrine Tumors. Cancer Discovery. 3(12). 1364–1377. 275 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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