Jun Lin
Impact in
- Immunology top 5%
- Infectious Diseases top 5%
- COVID-19 Clinical Research Studies
- SARS-CoV-2 and COVID-19 Research
Papers in
- Epidemiology 15
- Autophagy in Disease and Therapy 15
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- Single-cell and spatial transcriptomics 9
- Co-authors
- Longping Wen (18 shared papers)Yunjiao Zhang (10 shared papers)Peipei Jin (9 shared papers)Pengfei Wei (10 shared papers)Kun Qu (10 shared papers)Zhihai Huang (4 shared papers)Hao Wu (4 shared papers)Ning Gu (4 shared papers)
- Journals
- Biomaterials (4 papers)Nature Communications (4 papers)Small (3 papers)Nanoscale (3 papers)Advanced Functional Materials (2 papers)
- Partner nations
- ChinaUnited StatesIndia
In The Last Decade
Jun Lin
43 papers receiving 2.8k citations
Jun Lin's Hit Papers
Peers
Comparison fields: 5 of 134
- Immunology 508
- Infectious Diseases 376
- Biomaterials 211
- Molecular Biology 1.1k
- Epidemiology 488
Countries citing papers authored by Jun Lin
This map shows the geographic impact of Jun Lin'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 Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Lin more than expected).
Fields of papers citing papers by Jun Lin
This network shows the impact of papers produced by Jun Lin. 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 Lin. The network helps show where Jun Lin may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Lin, 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 43 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas Hit paper breakdown → | 2021 | 632 |
| 2 | Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution Hit paper breakdown → | 2022 | 271 |
| 3 | 2014 | 248 | |
| 4 | 2018 | 189 | |
| 5 | 2020 | 166 | |
| 6 | 2021 | 152 | |
| 7 | 2022 | 115 | |
| 8 | 2016 | 108 | |
| 9 | 2012 | 82 | |
| 10 | 2019 | 76 | |
| 11 | 2023 | 71 | |
| 12 | 2019 | 66 | |
| 13 | 2015 | 66 | |
| 14 | 2014 | 64 | |
| 15 | 2018 | 48 | |
| 16 | 2013 | 47 | |
| 17 | 2016 | 46 | |
| 18 | 2016 | 40 | |
| 19 | 2020 | 39 | |
| 20 | 2021 | 39 |
About Jun Lin
Jun Lin is a scholar working on Epidemiology, Molecular Biology, Immunology, Materials Chemistry and Astronomy and Astrophysics, having authored 43 papers that have together received 2.9k indexed citations. Recurring topics across this work include Autophagy in Disease and Therapy (15 papers), Single-cell and spatial transcriptomics (9 papers), Solar and Space Plasma Dynamics (4 papers), COVID-19 Clinical Research Studies (3 papers), Advanced Nanomaterials in Catalysis (3 papers), Nanoparticles: synthesis and applications (3 papers), Nanoplatforms for cancer theranostics (3 papers) and Cannabis and Cannabinoid Research (2 papers). The work is most often cited by research in Immunology (508 citations), Infectious Diseases (376 citations), Biomaterials (211 citations), Molecular Biology (1.1k citations) and Epidemiology (488 citations). Jun Lin has collaborated with scholars based in China, United States and India. Frequent co-authors include Longping Wen, Yunjiao Zhang, Peipei Jin, Pengfei Wei, Kun Qu, Zhihai Huang, Hao Wu, Ning Gu, Jiqian Zhang and Wei Zhou. Their work appears in journals such as Biomaterials, Nature Communications, Small, Nanoscale and Advanced Functional Materials.
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.