Jun Xia
Impact in
- Aquatic Science top 2%
- Aquaculture Nutrition and Growth
- Genetics top 2%
- Blood disorders and treatments
- Genetic and phenotypic traits in livestock
- Genetic diversity and population structure
Papers in
- Genetics 26
- Blood disorders and treatments 7
- Bacterial Genetics and Biotechnology 6
- Genetic diversity and population structure 6
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- Genomics and Phylogenetic Studies 8
- DNA Repair Mechanisms 7
- Co-authors
- Gen Hua Yue (10 shared papers)Daniel C. Link (10 shared papers)Grace Lin (6 shared papers)Fei Sun (4 shared papers)David C. Dale (4 shared papers)Zi Yi Wan (4 shared papers)David S. Grenda (2 shared papers)Mark A. Murakami (2 shared papers)
- Journals
- Blood (8 papers)INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY (5 papers)Fish & Shellfish Immunology (3 papers)BMC Genomics (3 papers)Science Advances (3 papers)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Jun Xia
47 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 112
- Aquatic Science 248
- Genetics 738
- Immunology 470
- Molecular Medicine 73
- Physiology 62
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 50 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 137 | |
| 2 | 2019 | 129 | |
| 3 | 2009 | 111 | |
| 4 | 2011 | 100 | |
| 5 | 2013 | 92 | |
| 6 | 2010 | 86 | |
| 7 | 2018 | 69 | |
| 8 | 2011 | 60 | |
| 9 | 2016 | 59 | |
| 10 | 2014 | 57 | |
| 11 | 2013 | 55 | |
| 12 | 2010 | 46 | |
| 13 | 2008 | 37 | |
| 14 | 2014 | 34 | |
| 15 | 2012 | 32 | |
| 16 | 2015 | 32 | |
| 17 | 2016 | 30 | |
| 18 | 2010 | 28 | |
| 19 | 2011 | 25 | |
| 20 | 2022 | 24 |
About Jun Xia
Jun Xia is a scholar working on Genetics, Molecular Biology, Immunology, Ecology and Aquatic Science, having authored 50 papers that have together received 1.5k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (8 papers), Aquaculture disease management and microbiota (8 papers), DNA Repair Mechanisms (7 papers), Blood disorders and treatments (7 papers), Bacterial Genetics and Biotechnology (6 papers), Genetic diversity and population structure (6 papers), Microbial Community Ecology and Physiology (5 papers) and interferon and immune responses (4 papers). The work is most often cited by research in Aquatic Science (248 citations), Genetics (738 citations), Immunology (470 citations), Molecular Medicine (73 citations) and Physiology (62 citations). Jun Xia has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Gen Hua Yue, Daniel C. Link, Grace Lin, Fei Sun, David C. Dale, Zi Yi Wan, David S. Grenda, Mark A. Murakami, Ze Yuan Zhu and Qiuming Liu. Their work appears in journals such as Blood, INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY, Fish & Shellfish Immunology, BMC Genomics and Science Advances.
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.