Jay Yang
Impact in
- Hematology top 5%
- Acute Myeloid Leukemia Research
- Molecular Biology top 10%
- DNA Repair Mechanisms
- Histone Deacetylase Inhibitors Research
- Protein Degradation and Inhibitors
- Epigenetics and DNA Methylation
- Cancer therapeutics and mechanisms
- Ubiquitin and proteasome pathways
Papers in ⓘ
- Hematology 46
- Acute Myeloid Leukemia Research 35
- Chronic Myeloid Leukemia Treatments 16
- Genetics 20
- Myeloproliferative Neoplasms: Diagnosis and Treatment 13
- Co-authors
- Grant W. Brown (8 shared papers)Ian D. Hickson (3 shared papers)Csanád Z. Bachrati (3 shared papers)Yongwei Su (7 shared papers)Yubin Ge (7 shared papers)Holly Edwards (7 shared papers)Jeffrey W. Taub (7 shared papers)Alexandra Sobeck (1 shared paper)
- Journals
- Blood (35 papers)Journal of Clinical Oncology (10 papers)Hematological Oncology (3 papers)Journal of Pain (3 papers)Haematologica (2 papers)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Jay Yang
92 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 93
- Hematology 318
- Molecular Biology 810
- Cancer Research 156
- Oncology 281
- Genetics 103
Countries citing papers authored by Jay Yang
This map shows the geographic impact of Jay Yang'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 Jay Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay Yang more than expected).
Fields of papers citing papers by Jay Yang
This network shows the impact of papers produced by Jay Yang. 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 Jay Yang. The network helps show where Jay Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Jay Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 103 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 165 | |
| 2 | 2020 | 143 | |
| 3 | 2002 | 86 | |
| 4 | 2022 | 85 | |
| 5 | 2005 | 78 | |
| 6 | 2010 | 57 | |
| 7 | 2019 | 56 | |
| 8 | 2019 | 48 | |
| 9 | 2020 | 38 | |
| 10 | 2014 | 35 | |
| 11 | 2018 | 30 | |
| 12 | 2012 | 27 | |
| 13 | 2012 | 26 | |
| 14 | 2022 | 26 | |
| 15 | 2016 | 23 | |
| 16 | 2018 | 20 | |
| 17 | 2022 | 18 | |
| 18 | 2015 | 17 | |
| 19 | 2019 | 16 | |
| 20 | 2022 | 16 |
About Jay Yang
Jay Yang is a scholar working on Hematology, Genetics, Pathology and Forensic Medicine, Molecular Biology and Oncology, having authored 103 papers that have together received 1.3k indexed citations. Recurring topics across this work include Acute Myeloid Leukemia Research (35 papers), Chronic Myeloid Leukemia Treatments (16 papers), Protein Degradation and Inhibitors (13 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (13 papers), Lymphoma Diagnosis and Treatment (13 papers), Histone Deacetylase Inhibitors Research (11 papers), Cancer-related gene regulation (11 papers) and DNA Repair Mechanisms (7 papers). The work is most often cited by research in Hematology (318 citations), Molecular Biology (810 citations), Cancer Research (156 citations), Oncology (281 citations) and Genetics (103 citations). Jay Yang has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Grant W. Brown, Ian D. Hickson, Csanád Z. Bachrati, Yongwei Su, Yubin Ge, Holly Edwards, Jeffrey W. Taub, Alexandra Sobeck, Rong Guo and Dongyi Xu. Their work appears in journals such as Blood, Journal of Clinical Oncology, Hematological Oncology, Journal of Pain and Haematologica.
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.