Jun Xia
Impact in
- Genetics top 5%
- Blood disorders and treatments
- Bacterial Genetics and Biotechnology
- Genetic Associations and Epidemiology
- Evolution and Genetic Dynamics
- Hematology top 10%
- Acute Myeloid Leukemia Research
Papers in
- Genetics 16
- Blood disorders and treatments 7
- Genetic Associations and Epidemiology 6
- Bacterial Genetics and Biotechnology 3
- BRCA gene mutations in cancer 2
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- DNA Repair Mechanisms 8
- Genomics and Chromatin Dynamics 1
- Co-authors
- Daniel C. Link (6 shared papers)David C. Dale (3 shared papers)Mark Alan Murakami (2 shared papers)David S. Grenda (2 shared papers)Laurence A. Boxer (1 shared paper)Mary C. Dinauer (1 shared paper)Andrew A.G. Aprikyan (2 shared papers)Audrey Anna Bolyard (2 shared papers)
- Journals
- Blood (5 papers)Science Advances (3 papers)Trends in Genetics (1 paper)Nature Communications (1 paper)Microbial Cell (1 paper)
- Partner nations
- United StatesFranceSweden
In The Last Decade
Jun Xia
23 papers receiving 1k citations
Peers
Comparison fields: 5 of 64
- Genetics 636
- Hematology 77
- Molecular Medicine 37
- Molecular Biology 257
- Immunology 55
Countries citing papers authored by Jun Xia
This map shows the geographic impact of Jun Xia'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 Jun Xia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Xia more than expected).
Fields of papers citing papers by Jun Xia
This network shows the impact of papers produced by Jun Xia. 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 Jun Xia. The network helps show where Jun Xia may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Xia, 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 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 173 | |
| 2 | 2009 | 145 | |
| 3 | 2019 | 142 | |
| 4 | 2017 | 101 | |
| 5 | 2011 | 73 | |
| 6 | 2022 | 71 | |
| 7 | 2019 | 70 | |
| 8 | 2019 | 44 | |
| 9 | 2008 | 43 | |
| 10 | 2019 | 43 | |
| 11 | 2016 | 36 | |
| 12 | 2021 | 23 | |
| 13 | 2023 | 22 | |
| 14 | 2021 | 17 | |
| 15 | 2016 | 12 | |
| 16 | 2024 | 11 | |
| 17 | 2019 | 9 | |
| 18 | 2023 | 6 | |
| 19 | 2024 | 6 | |
| 20 | 2018 | 5 |
About Jun Xia
Jun Xia is a scholar working on Genetics, Molecular Biology, Molecular Medicine, Cancer Research and Spectroscopy, having authored 24 papers that have together received 1.1k indexed citations. Recurring topics across this work include DNA Repair Mechanisms (8 papers), Blood disorders and treatments (7 papers), Genetic Associations and Epidemiology (6 papers), Bacterial Genetics and Biotechnology (3 papers), BRCA gene mutations in cancer (2 papers), Mass Spectrometry Techniques and Applications (1 paper), Cancer Genomics and Diagnostics (1 paper) and Genomics and Chromatin Dynamics (1 paper). The work is most often cited by research in Genetics (636 citations), Hematology (77 citations), Molecular Medicine (37 citations), Molecular Biology (257 citations) and Immunology (55 citations). Jun Xia has collaborated with scholars based in United States, France and Sweden. Frequent co-authors include Daniel C. Link, David C. Dale, Mark Alan Murakami, David S. Grenda, Laurence A. Boxer, Mary C. Dinauer, Andrew A.G. Aprikyan, Audrey Anna Bolyard, Elin Rodger and Susan M. Rosenberg. Their work appears in journals such as Blood, Science Advances, Trends in Genetics, Nature Communications and Microbial Cell.
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.