Kenneth J. Wilkins

2.7k total citations
46 papers, 1.0k citations indexed

About

Kenneth J. Wilkins is a scholar working on Infectious Diseases, Epidemiology and Molecular Biology. According to data from OpenAlex, Kenneth J. Wilkins has authored 46 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Infectious Diseases, 11 papers in Epidemiology and 7 papers in Molecular Biology. Recurrent topics in Kenneth J. Wilkins's work include COVID-19 Clinical Research Studies (8 papers), Chronic Kidney Disease and Diabetes (4 papers) and Diabetes and associated disorders (4 papers). Kenneth J. Wilkins is often cited by papers focused on COVID-19 Clinical Research Studies (8 papers), Chronic Kidney Disease and Diabetes (4 papers) and Diabetes and associated disorders (4 papers). Kenneth J. Wilkins collaborates with scholars based in United States, United Kingdom and Italy. Kenneth J. Wilkins's co-authors include Clinton K. Murray, Robert A. Star, Peter S.T. Yuen, Duane R. Hospenthal, Brian J. Eastridge, Lorne H. Blackbourne, Nancy C. Molter, Mary Ann Spott, Takayuki Tsuji and Irina N. Baranova and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and American Journal of Clinical Nutrition.

In The Last Decade

Kenneth J. Wilkins

46 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kenneth J. Wilkins United States 19 242 239 236 188 126 46 1.0k
Ming‐Che Lee Taiwan 21 281 1.2× 245 1.0× 479 2.0× 170 0.9× 49 0.4× 104 1.4k
Minoru Ando Japan 25 283 1.2× 219 0.9× 265 1.1× 273 1.5× 81 0.6× 99 1.6k
Yong‐Kwei Tsau Taiwan 20 218 0.9× 402 1.7× 183 0.8× 162 0.9× 53 0.4× 85 1.3k
Dominik Jarczak Germany 18 234 1.0× 478 2.0× 170 0.7× 279 1.5× 28 0.2× 56 1.3k
Idit F. Schwartz Israel 20 223 0.9× 127 0.5× 148 0.6× 485 2.6× 58 0.5× 71 1.8k
Tim Williams United Kingdom 18 308 1.3× 147 0.6× 377 1.6× 506 2.7× 245 1.9× 69 1.7k
Joost H.W. Rutten Netherlands 18 487 2.0× 262 1.1× 212 0.9× 83 0.4× 147 1.2× 46 1.5k
Shawn D. Larson United States 25 276 1.1× 504 2.1× 502 2.1× 75 0.4× 185 1.5× 72 1.7k
Abdoul Hamide India 17 96 0.4× 168 0.7× 105 0.4× 136 0.7× 202 1.6× 85 763
Marcelo Chen Taiwan 18 238 1.0× 164 0.7× 126 0.5× 255 1.4× 39 0.3× 95 1.1k

Countries citing papers authored by Kenneth J. Wilkins

Since Specialization
Citations

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

Fields of papers citing papers by Kenneth J. Wilkins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kenneth J. Wilkins. 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 Kenneth J. Wilkins. The network helps show where Kenneth J. Wilkins may publish in the future.

Co-authorship network of co-authors of Kenneth J. Wilkins

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth J. Wilkins. A scholar is included among the top collaborators of Kenneth J. Wilkins 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 Kenneth J. Wilkins. Kenneth J. Wilkins 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.
Bhatia, Abhishek, Alexander Preiss, M. Daniel Brannock, et al.. (2025). Effect of nirmatrelvir/ritonavir (Paxlovid) on hospitalization among adults with COVID-19: An electronic health record-based target trial emulation from N3C. PLoS Medicine. 22(1). e1004493–e1004493. 2 indexed citations
2.
Hurwitz, Eric, Cara D Varley, Alfred Anzalone, et al.. (2025). Identifying People Living With or Those at Risk for HIV in a Nationally Sampled Electronic Health Record Repository Called the National Clinical Cohort Collaborative: Computational Phenotyping Study. JMIR Medical Informatics. 13. e68143–e68143. 1 indexed citations
3.
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5.
O’Neil, Shawn T., Charisse Madlock‐Brown, Kenneth J. Wilkins, et al.. (2024). Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs. npj Digital Medicine. 7(1). 296–296. 2 indexed citations
6.
Violet, Pierre-Christian, Hongbin Tu, Yu Wang, et al.. (2024). Altered RBC deformability in diabetes: clinical characteristics and RBC pathophysiology. Cardiovascular Diabetology. 23(1). 370–370. 5 indexed citations
7.
Coleman, Ben, Elena Casiraghi, Tiffany J. Callahan, et al.. (2024). Association of post-COVID phenotypic manifestations with new-onset psychiatric disease. Translational Psychiatry. 14(1). 246–246. 1 indexed citations
8.
Kimmel, Paul L., Thomas D. Nolin, Ivonne Hernandez Schulman, et al.. (2024). Opioid Prescriptions for US Patients Undergoing Long-Term Dialysis or with Kidney Transplant from 2011 to 2020. Journal of the American Society of Nephrology. 36(1). 108–121. 1 indexed citations
9.
Blau, Hannah, Elena Casiraghi, Johanna Loomba, et al.. (2023). Predictive models of long COVID. EBioMedicine. 96. 104777–104777. 18 indexed citations
10.
Wong, Rachel, Emily K.Y. Lam, Carolyn T. Bramante, et al.. (2023). Does COVID-19 Infection Increase the Risk of Diabetes? Current Evidence. Current Diabetes Reports. 23(8). 207–216. 21 indexed citations
11.
Schulman, Ivonne Hernandez, Kevin Chan, Kenneth J. Wilkins, et al.. (2023). Readmission and Mortality After Hospitalization With Acute Kidney Injury. American Journal of Kidney Diseases. 82(1). 63–74.e1. 16 indexed citations
12.
Rankin, Summer K., et al.. (2022). A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation. Kidney360. 3(9). 1556–1565. 12 indexed citations
13.
Bramante, Carolyn T., Steve Johnson, Víctor García, et al.. (2022). Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study. PLoS ONE. 17(11). e0271574–e0271574. 5 indexed citations
14.
Andargie, T., Takayuki Tsuji, Fayaz Seifuddin, et al.. (2021). Cell-free DNA maps COVID-19 tissue injury and risk of death and can cause tissue injury. JCI Insight. 6(7). 88 indexed citations
15.
Zhu, Lu, Diptadip Dattaroy, Jonathan Pham, et al.. (2019). Intraislet glucagon signaling is critical for maintaining glucose homeostasis. JCI Insight. 4(10). 116 indexed citations
16.
Zhao, Xiao-Nan, Yong‐Wei Zhang, Kenneth J. Wilkins, Winfried Edelmann, & Karen Usdin. (2018). MutLγ promotes repeat expansion in a Fragile X mouse model while EXO1 is protective. PLoS Genetics. 14(10). e1007719–e1007719. 44 indexed citations
17.
Souza, Ana C. P., Alexander V. Bocharov, Irina N. Baranova, et al.. (2016). Antagonism of scavenger receptor CD36 by 5A peptide prevents chronic kidney disease progression in mice independent of blood pressure regulation. Kidney International. 89(4). 809–822. 62 indexed citations
18.
Burgess, Timothy, Clinton K. Murray, Mary Bavaro, et al.. (2015). Self-administration of intranasal influenza vaccine: Immunogenicity and volunteer acceptance. Vaccine. 33(32). 3894–3899. 16 indexed citations
19.
Rodríguez, Carlos J., Amy Weintrob, Jinesh Shah, et al.. (2014). Risk Factors Associated with Invasive Fungal Infections in Combat Trauma. Surgical Infections. 15(5). 521–526. 41 indexed citations
20.

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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