Kevin H. Mayo

24.1k total citations
283 papers, 10.5k citations indexed

About

Kevin H. Mayo is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Kevin H. Mayo has authored 283 papers receiving a total of 10.5k indexed citations (citations by other indexed papers that have themselves been cited), including 190 papers in Molecular Biology, 83 papers in Immunology and 29 papers in Oncology. Recurrent topics in Kevin H. Mayo's work include Galectins and Cancer Biology (62 papers), Glycosylation and Glycoproteins Research (60 papers) and Protein Structure and Dynamics (33 papers). Kevin H. Mayo is often cited by papers focused on Galectins and Cancer Biology (62 papers), Glycosylation and Glycoproteins Research (60 papers) and Protein Structure and Dynamics (33 papers). Kevin H. Mayo collaborates with scholars based in United States, China and Netherlands. Kevin H. Mayo's co-authors include Arjan W. Griffioen, Ruud P.M. Dings, Vladimir A. Daragan, Michelle C. Miller, Irina V. Nesmelova, Alison Goate, Daisy W.J. van der Schaft, Elena Ilyina, John C. Morris and Judith R. Haseman and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Journal of Biological Chemistry.

In The Last Decade

Kevin H. Mayo

276 papers receiving 10.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kevin H. Mayo United States 55 6.0k 3.1k 1.7k 1.4k 795 283 10.5k
K. Ravi Acharya United Kingdom 58 7.1k 1.2× 1.9k 0.6× 1.0k 0.6× 763 0.5× 1.2k 1.5× 272 12.0k
Barbara C. Furie United States 58 4.1k 0.7× 2.1k 0.7× 1.0k 0.6× 632 0.4× 349 0.4× 156 14.8k
Giampietro Ramponi Italy 51 9.0k 1.5× 1.4k 0.5× 627 0.4× 3.3k 2.4× 478 0.6× 209 11.3k
Dennis E. Hallahan United States 66 6.5k 1.1× 1.3k 0.4× 3.2k 1.9× 311 0.2× 423 0.5× 267 13.3k
Fuyuhiko Inagaki Japan 64 7.3k 1.2× 2.3k 0.8× 853 0.5× 853 0.6× 636 0.8× 329 14.6k
David Piwnica‐Worms United States 71 9.2k 1.5× 3.3k 1.1× 5.8k 3.5× 645 0.5× 266 0.3× 282 19.5k
James W. Dennis Canada 63 10.8k 1.8× 5.5k 1.8× 2.0k 1.2× 608 0.4× 268 0.3× 206 15.8k
David M. Hockenbery United States 50 10.5k 1.7× 3.2k 1.1× 3.6k 2.2× 973 0.7× 377 0.5× 124 16.7k
Robert Roskoski United States 57 9.9k 1.6× 1.3k 0.4× 4.1k 2.5× 773 0.5× 390 0.5× 173 16.3k
John E. Shively United States 74 9.8k 1.6× 2.2k 0.7× 4.3k 2.6× 801 0.6× 175 0.2× 420 20.1k

Countries citing papers authored by Kevin H. Mayo

Since Specialization
Citations

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

Fields of papers citing papers by Kevin H. Mayo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin H. Mayo. 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 Kevin H. Mayo. The network helps show where Kevin H. Mayo may publish in the future.

Co-authorship network of co-authors of Kevin H. Mayo

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin H. Mayo. A scholar is included among the top collaborators of Kevin H. Mayo 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 Kevin H. Mayo. Kevin H. Mayo 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.
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Gao, Hui, et al.. (2025). Multifunctional Metal Composite Hydrogels for Diabetic Wound Therapy. Gels. 11(12). 960–960.
3.
Wu, Jing, et al.. (2024). Application of an α-galactosidase from Bacteroides fragilis on structural analysis of raffinose family oligosaccharides. Carbohydrate Polymers. 346. 122661–122661. 3 indexed citations
4.
Zhao, Zihan, Jing Wu, Xuejiao Xu, et al.. (2024). Oligosaccharides from Stellaria dichotoma L. var. lanceolate bind to galectin-3 and ameliorate effects of colitis. Carbohydrate Polymers. 345. 122551–122551. 4 indexed citations
5.
Wang, Yibing, Weiyang Wang, Kevin H. Mayo, et al.. (2024). A trapped covalent intermediate as a key catalytic element in the hydrolysis of a GH3 β-glucosidase: An X-ray crystallographic and biochemical study. International Journal of Biological Macromolecules. 265(Pt 2). 131131–131131. 4 indexed citations
6.
Mayo, Kevin H., et al.. (2024). CD98hc, a novel of galectin-8 receptor, binds to galectin-8 in an N-glycosylation-dependent manner. Acta Biochimica et Biophysica Sinica. 57(5). 749–757.
7.
Lin, Zhiying, et al.. (2023). The model polysaccharide potato galactan is actually a mixture of different polysaccharides. Carbohydrate Polymers. 313. 120889–120889. 12 indexed citations
8.
Meng, Yue, et al.. (2023). Galactan induces macrophage M0 to M1 conversion to combat colon cancer. Food Science and Technology Research. 30(1). 75–82. 2 indexed citations
9.
Xu, Xuejiao, Hairong Cheng, Michelle C. Miller, et al.. (2021). Galectin-3 N-terminal tail prolines modulate cell activity and glycan-mediated oligomerization/phase separation. Proceedings of the National Academy of Sciences. 118(19). 39 indexed citations
10.
Dings, Ruud P.M., et al.. (2013). Bacterial membrane disrupting dodecapeptide SC4 improves survival of mice challenged with Pseudomonas aeruginosa. Biochimica et Biophysica Acta (BBA) - General Subjects. 1830(6). 3454–3457. 8 indexed citations
11.
Dings, Ruud P.M., Kieng B. Vang, Karolien Castermans, et al.. (2011). Enhancement of T-cell–Mediated Antitumor Response: Angiostatic Adjuvant to Immunotherapy against Cancer. Clinical Cancer Research. 17(10). 3134–3145. 59 indexed citations
12.
Kauwe, John, Carlos Cruchaga, Celeste M. Karch, et al.. (2011). Fine Mapping of Genetic Variants in BIN1, CLU, CR1 and PICALM for Association with Cerebrospinal Fluid Biomarkers for Alzheimer's Disease. PLoS ONE. 6(2). e15918–e15918. 45 indexed citations
13.
Kauwe, John, Carlos Cruchaga, Sarah Bertelsen, et al.. (2010). Validating Predicted Biological Effects of Alzheimer's Disease Associated SNPs Using CSF Biomarker Levels. Journal of Alzheimer s Disease. 21(3). 833–842. 33 indexed citations
14.
Kauwe, John, Jun Wang, Kevin H. Mayo, et al.. (2008). Alzheimer’s disease risk variants show association with cerebrospinal fluid amyloid beta. Neurogenetics. 10(1). 13–17. 62 indexed citations
15.
Hellebrekers, Debby M.E.I., Karolien Castermans, Emmanuelle Viré, et al.. (2006). Epigenetic Regulation of Tumor Endothelial Cell Anergy: Silencing of Intercellular Adhesion Molecule-1 by Histone Modifications. Cancer Research. 66(22). 10770–10777. 129 indexed citations
16.
17.
Mayo, Kevin H. & Elena Ilyina. (1998). A folding pathway for βpep-4 peptide 33mer. Protein Science. 7(2). 1 indexed citations
18.
Mayo, Kevin H.. (1997). [30] Solution nuclear magnetic resonance characterization of peptide folding. Methods in enzymology on CD-ROM/Methods in enzymology. 289. 646–672. 4 indexed citations
19.
Varnum, James M., Kevin H. Mayo, Andrew V. Schally, & Mathew L. Thakur. (1994). Tc-99m labeled somatostatin analogue, RC-160: 1H-NMR and computer modeling conformational analysis. Journal of Labelled Compounds and Radiopharmaceuticals. 35. 549–551.
20.
Mayo, Kevin H., et al.. (1991). Human platelet factor 4 subunit association/dissociation thermodynamics and kinetics. Biochemistry. 30(26). 6402–6411. 26 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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