Sho Iketani
- Infectious Diseases top 0.5%
- Molecular Biology
- Animal Science and Zoology top 2%
- Immunology top 10%
- Computational Theory and Mathematics top 2%
- Co-authors
- David D. HoYicheng GuoZizhang ShengLihong LiuJian YuMaple WangYaoxing HuangQian Wang
- Topics
- SARS-CoV-2 and COVID-19 Research (15 papers)COVID-19 Clinical Research Studies (6 papers)Viral gastroenteritis research and epidemiology (4 papers)
- Journals
- NatureCellNature Communications
- Partner nations
- United StatesHong KongJapan
In The Last Decade
Sho Iketani
25 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 105
- Infectious Diseases 1.9k
- Molecular Biology 673
- Animal Science and Zoology 286
- Immunology 236
- Computational Theory and Mathematics 234
Countries citing papers authored by Sho Iketani
This map shows the geographic impact of Sho Iketani'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 Sho Iketani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sho Iketani more than expected).
Fields of papers citing papers by Sho Iketani
This network shows the impact of papers produced by Sho Iketani. 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 Sho Iketani. The network helps show where Sho Iketani may publish in the future.
Co-authorship network of co-authors of Sho Iketani
This figure shows the co-authorship network connecting the top 25 collaborators of Sho Iketani. A scholar is included among the top collaborators of Sho Iketani 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 Sho Iketani. Sho Iketani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 14 | |
| 3 | 13 | |
| 4 | Antigenicity and receptor affinity of SARS-CoV-2 BA.2.86 spikebreakdown → | 100 |
| 5 | 12 | |
| 6 | Antibody evasion properties of SARS-CoV-2 Omicron sublineagesbreakdown → | 478 |
| 7 | Antibody evasion by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4 and BA.5breakdown → | 433 |
| 8 | Multiple pathways for SARS-CoV-2 resistance to nirmatrelvirbreakdown → | 260 |
| 9 | Alarming antibody evasion properties of rising SARS-CoV-2 BQ and XBB subvariantsbreakdown → | 495 |
| 10 | 32 | |
| 11 | 58 | |
| 12 | 22 | |
| 13 | 8 | |
| 14 | 79 | |
| 15 | 34 | |
| 16 | 19 | |
| 17 | 5 | |
| 18 | 53 | |
| 19 | 15 | |
| 20 | 29 |
About Sho Iketani
Sho Iketani is a scholar working on Infectious Diseases, Computational Theory and Mathematics and Immunology, having authored 25 papers that have together received 2.3k indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (15 papers), COVID-19 Clinical Research Studies (6 papers) and Viral gastroenteritis research and epidemiology (4 papers). The work is most often cited by research in Infectious Diseases (1.9k citations), Animal Science and Zoology (286 citations) and Modeling and Simulation (86 citations). Sho Iketani has collaborated with scholars based in United States, Hong Kong and Japan. Frequent co-authors include David D. Ho, Yicheng Guo, Zizhang Sheng, Lihong Liu, Jian Yu, Maple Wang, Yaoxing Huang, Qian Wang, Zhiteng Li and Harris H. Wang. Their work appears in journals such as Nature, Cell and Nature Communications.
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.