Yun Dai
Impact in
- Hematology top 0.5%
- Multiple Myeloma Research and Treatments
- Acute Myeloid Leukemia Research
- Oncology top 1%
- Cancer-related Molecular Pathways
Papers in
- Hematology 56
- Multiple Myeloma Research and Treatments 34
- Acute Myeloid Leukemia Research 15
- Oncology 57
- Cancer-related Molecular Pathways 23
- Co-authors
- Steven GrantPaul DentMohamed RahmaniXin‐Yan PeiShuang ChenRoberto R. RosatoJorge A. AlmenaraLora B. Kramer
- Journals
- Blood (28 papers)Cancer Research (10 papers)Molecular Pharmacology (8 papers)Cancer Biology & Therapy (7 papers)Clinical Cancer Research (5 papers)
- Partner nations
- United StatesChinaRussia
In The Last Decade
Yun Dai
191 papers receiving 7.7k citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Hematology 1.4k
- Oncology 2.2k
- Cancer Research 1.2k
- Molecular Biology 5.5k
- Genetics 557
Countries citing papers authored by Yun Dai
This map shows the geographic impact of Yun Dai'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 Yun Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yun Dai more than expected).
Fields of papers citing papers by Yun Dai
This network shows the impact of papers produced by Yun Dai. 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 Yun Dai. The network helps show where Yun Dai may publish in the future.
Co-authors
The 25 scholars most cited alongside Yun Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 30 | |
| 4 | 2023 | 178 | |
| 5 | 2023 | 1 | |
| 6 | 2022 | 8 | |
| 7 | 2022 | 4 | |
| 8 | 2022 | 9 | |
| 9 | 2021 | 27 | |
| 10 | 2021 | 112 | |
| 11 | 2021 | 37 | |
| 12 | 2021 | 152 | |
| 13 | 2020 | 8 | |
| 14 | 2019 | 33 | |
| 15 | 2013 | 46 | |
| 16 | 2012 | 46 | |
| 17 | 2011 | 70 | |
| 18 | 2011 | 13 | |
| 19 | 2008 | 68 | |
| 20 | The MEK1/2 inhibitor AZD6244 (ARRY-142886) interacts synergistically with the novel Chk1 inhibitor AZD7762 to induce apoptosis in human multiple myeloma cells | 2008 | 3 |
About Yun Dai
Yun Dai is a scholar working on Hematology, Oncology, Cancer Research, Molecular Biology and Leadership and Management, having authored 202 papers that have together received 7.8k indexed citations. Recurring topics across this work include Histone Deacetylase Inhibitors Research (35 papers), Multiple Myeloma Research and Treatments (34 papers), Ubiquitin and proteasome pathways (29 papers), Protein Degradation and Inhibitors (29 papers), Cell death mechanisms and regulation (28 papers), Cancer-related Molecular Pathways (23 papers), Acute Myeloid Leukemia Research (15 papers) and NF-κB Signaling Pathways (12 papers). The work is most often cited by research in Hematology (1.4k citations), Oncology (2.2k citations), Cancer Research (1.2k citations), Molecular Biology (5.5k citations) and Genetics (557 citations). Yun Dai has collaborated with scholars based in United States, China and Russia. Frequent co-authors include Steven Grant, Paul Dent, Mohamed Rahmani, Xin‐Yan Pei, Shuang Chen, Roberto R. Rosato, Jorge A. Almenara, Lora B. Kramer, Prithviraj Bose and Hisashi Harada. Their work appears in journals such as Blood, Cancer Research, Molecular Pharmacology, Cancer Biology & Therapy and Clinical Cancer 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.