Kun Yao

105 papers receiving 2.0k citations

Kun Yao's Hit Papers

Targeting p53–MDM2 interaction by small-molecule inhibitors: learning from MDM2 inhibitors in clinical trials 2022 · 150 citations
1500+1+2Years since publication50100150

Peers

Kun Yao
Comparison fields: 5 of 112
  • Genetics 538
  • Cancer Research 259
  • Oncology 441
  • Immunology 236
  • Epidemiology 289
Replace Andrés Caicedo with:
Andrés Caicedo Ecuador
Ruty Mehrian‐Shai United States
Paola Izzo Italy
Dan Grisaru Israel
Emmanuelle Fantino Australia
Terzah M. Horton United States
Chad Torrice United States
Dirk Hendriks Belgium
Harry E. Gruber United States
Kun Yao relative to Andrés Caicedo Ecuador Andrés Caicedo's profile →
Citations per field
00.5×3.9×
Andrés Caicedo · 1×
Citations per year

Countries citing papers authored by Kun Yao

Since Specialization
Citations

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

Fields of papers citing papers by Kun Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kun Yao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kun Yao Line = papers co-authored together Kun Yao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 108 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014152
2
Targeting p53–MDM2 interaction by small-molecule inhibitors: learning from MDM2 inhibitors in clinical trials
Hit paper breakdown →
2022150
3 2015127
4 201690
5 201884
6 201782
7 201257
8 201857
9 202353
10 201549
11 201447
12 201939
13 201439
14 201138
15 201638
16 201432
17 201326
18 200625
19 200925
20 201624

About Kun Yao

Kun Yao is a scholar working on Molecular Biology, Genetics, Oncology, Epidemiology and Immunology, having authored 108 papers that have together received 2.0k indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (26 papers), Cytomegalovirus and herpesvirus research (16 papers), Viral-associated cancers and disorders (14 papers), Herpesvirus Infections and Treatments (7 papers), Immune Cell Function and Interaction (7 papers), Immunotherapy and Immune Responses (5 papers), Histone Deacetylase Inhibitors Research (5 papers) and MicroRNA in disease regulation (4 papers). The work is most often cited by research in Genetics (538 citations), Cancer Research (259 citations), Oncology (441 citations), Immunology (236 citations) and Epidemiology (289 citations). Kun Yao has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Tao Jiang, Changxiang Yan, Shouwei Li, Yinyan Wang, Chenxing Wu, Xiaoxia Peng, Yongping You, Pengfei Wang, Pei Yang and Haohao Zhu. Their work appears in journals such as Oncotarget, Journal of Virology, Cellular and Molecular Immunology, Journal of Medicinal Chemistry and Cancer Management and Research.

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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