Jun Lin
Impact in
- Biomaterials top 5%
- Nanoparticle-Based Drug Delivery
- Physiology top 5%
Papers in
-
- Single-cell and spatial transcriptomics 7
- Epidemiology 15
- Autophagy in Disease and Therapy 15
- Co-authors
- Longping Wen (18 shared papers)Yunjiao Zhang (10 shared papers)Pengfei Wei (10 shared papers)Peipei Jin (9 shared papers)Hao Wu (4 shared papers)Ning Gu (4 shared papers)Zhihai Huang (4 shared papers)Jiqian Zhang (7 shared papers)
- Journals
- Nature Communications (4 papers)Biomaterials (4 papers)Small (3 papers)Nanoscale (3 papers)Cell Discovery (2 papers)
- Partner nations
- ChinaUnited StatesTaiwan
In The Last Decade
Jun Lin
40 papers receiving 1.7k citations
Jun Lin's Hit Papers
Peers
Comparison fields: 5 of 120
- Biomaterials 205
- Physiology 67
- Immunology 269
- Epidemiology 435
- Biomedical Engineering 505
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 40 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 225 | |
| 2 | Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution Hit paper breakdown → | 2022 | 225 |
| 3 | 2018 | 178 | |
| 4 | 2021 | 128 | |
| 5 | 2016 | 101 | |
| 6 | 2012 | 78 | |
| 7 | 2019 | 71 | |
| 8 | 2015 | 63 | |
| 9 | 2019 | 63 | |
| 10 | 2014 | 56 | |
| 11 | 2023 | 49 | |
| 12 | 2013 | 46 | |
| 13 | 2018 | 44 | |
| 14 | 2016 | 41 | |
| 15 | 2016 | 38 | |
| 16 | 2020 | 37 | |
| 17 | 2016 | 35 | |
| 18 | 2016 | 31 | |
| 19 | 2015 | 31 | |
| 20 | 2015 | 29 |
About Jun Lin
Jun Lin is a scholar working on Molecular Biology, Epidemiology, Materials Chemistry, Immunology and Pathology and Forensic Medicine, having authored 40 papers that have together received 1.8k indexed citations. Recurring topics across this work include Autophagy in Disease and Therapy (15 papers), Single-cell and spatial transcriptomics (7 papers), Nanoplatforms for cancer theranostics (3 papers), Advanced Nanomaterials in Catalysis (3 papers), Nanoparticles: synthesis and applications (3 papers), Solar and Space Plasma Dynamics (3 papers), Nanoparticle-Based Drug Delivery (2 papers) and Ionosphere and magnetosphere dynamics (2 papers). The work is most often cited by research in Biomaterials (205 citations), Physiology (67 citations), Immunology (269 citations), Epidemiology (435 citations) and Biomedical Engineering (505 citations). Jun Lin has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Longping Wen, Yunjiao Zhang, Pengfei Wei, Peipei Jin, Hao Wu, Ning Gu, Zhihai Huang, Jiqian Zhang, Kun Qu and Wei Zhou. Their work appears in journals such as Nature Communications, Biomaterials, Small, Nanoscale and Cell Discovery.
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.