Maple Wang

6.3k total citations · 4 hit papers
9 papers, 1.9k citations indexed

About

Maple Wang is a scholar working on Infectious Diseases, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Maple Wang has authored 9 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Infectious Diseases, 3 papers in Molecular Biology and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Maple Wang's work include SARS-CoV-2 and COVID-19 Research (8 papers), Viral gastroenteritis research and epidemiology (3 papers) and COVID-19 Clinical Research Studies (3 papers). Maple Wang is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (8 papers), Viral gastroenteritis research and epidemiology (3 papers) and COVID-19 Clinical Research Studies (3 papers). Maple Wang collaborates with scholars based in United States, Hong Kong and Canada. Maple Wang's co-authors include David D. Ho, Jian Yu, Lihong Liu, Yaoxing Huang, Yicheng Guo, Zizhang Sheng, Sho Iketani, Manoj S. Nair, Harris H. Wang and Yiming Huang and has published in prestigious journals such as Nature, Cell and Science Translational Medicine.

In The Last Decade

Maple Wang

9 papers receiving 1.8k citations

Hit Papers

Alarming antibody evasion properties of rising SARS-CoV-2... 2021 2026 2022 2024 2022 2022 2022 2021 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maple Wang United States 7 1.7k 515 243 207 157 9 1.9k
Paul W. Rothlauf United States 9 1.7k 1.0× 535 1.0× 240 1.0× 184 0.9× 206 1.3× 12 2.0k
Amy Kempf Germany 12 1.5k 0.9× 380 0.7× 206 0.8× 134 0.6× 160 1.0× 27 1.6k
Luise Graichen Germany 10 1.4k 0.8× 356 0.7× 210 0.9× 130 0.6× 146 0.9× 21 1.6k
Sho Iketani United States 18 1.9k 1.1× 673 1.3× 286 1.2× 214 1.0× 236 1.5× 25 2.3k
Qiyu Sun China 14 1.5k 0.9× 637 1.2× 272 1.1× 138 0.7× 174 1.1× 25 2.0k
Ray T. Y. So Hong Kong 9 1.2k 0.7× 345 0.7× 215 0.9× 209 1.0× 135 0.9× 17 1.4k
Frauke Muecksch United States 12 1.3k 0.8× 322 0.6× 151 0.6× 164 0.8× 198 1.3× 20 1.5k
Anthony Bowen United States 14 1.0k 0.6× 392 0.8× 147 0.6× 256 1.2× 192 1.2× 22 1.4k
Zhiteng Li China 11 998 0.6× 370 0.7× 164 0.7× 135 0.7× 132 0.8× 32 1.3k
Inga Nehlmeier Germany 22 1.3k 0.8× 471 0.9× 204 0.8× 129 0.6× 306 1.9× 49 1.9k

Countries citing papers authored by Maple Wang

Since Specialization
Citations

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

Fields of papers citing papers by Maple Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maple Wang

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

All Works

9 of 9 papers shown
1.
Aschner, Clare Burn, Krithika Muthuraman, Iga Kucharska, et al.. (2023). A multi-specific, multi-affinity antibody platform neutralizes sarbecoviruses and confers protection against SARS-CoV-2 in vivo. Science Translational Medicine. 15(697). eadf4549–eadf4549. 11 indexed citations
2.
Nair, Manoj S., Ruy M. Ribeiro, Maple Wang, et al.. (2023). Changes in serum-neutralizing antibody potency and breadth post-SARS-CoV-2 mRNA vaccine boost. iScience. 26(4). 106345–106345. 3 indexed citations
3.
Sheng, Zizhang, Jude Bimela, Maple Wang, et al.. (2023). An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design. Frontiers in Immunology. 14. 1190416–1190416. 2 indexed citations
4.
Iketani, Sho, Lihong Liu, Yicheng Guo, et al.. (2022). Antibody evasion properties of SARS-CoV-2 Omicron sublineages. Nature. 604(7906). 553–556. 478 indexed citations breakdown →
5.
Wang, Qian, Yicheng Guo, Sho Iketani, et al.. (2022). Antibody evasion by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4 and BA.5. Nature. 608(7923). 603–608. 433 indexed citations breakdown →
6.
Wang, Qian, Sho Iketani, Zhiteng Li, et al.. (2022). Alarming antibody evasion properties of rising SARS-CoV-2 BQ and XBB subvariants. Cell. 186(2). 279–286.e8. 495 indexed citations breakdown →
7.
Cerutti, Gabriele, Yicheng Guo, Pengfei Wang, et al.. (2021). Neutralizing antibody 5-7 defines a distinct site of vulnerability in SARS-CoV-2 spike N-terminal domain. Cell Reports. 37(5). 109928–109928. 42 indexed citations
8.
Wang, Pengfei, Ryan G. Casner, Manoj S. Nair, et al.. (2021). Increased resistance of SARS-CoV-2 variant P.1 to antibody neutralization. Cell Host & Microbe. 29(5). 747–751.e4. 390 indexed citations breakdown →
9.
Wang, Pengfei, Ryan G. Casner, Manoj S. Nair, et al.. (2021). A monoclonal antibody that neutralizes SARS-CoV-2 variants, SARS-CoV, and other sarbecoviruses. Emerging Microbes & Infections. 11(1). 147–157. 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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