Ge Yan
Impact in
- Computer Science Applications top 10%
- Teaching and Learning Programming
- Human-Computer Interaction top 10%
Papers in
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- Extracellular vesicles in disease 4
- RNA Interference and Gene Delivery 2
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- Topic Modeling 2
- Co-authors
- Li‐Hua Peng (8 shared papers)Minhong Tan (5 shared papers)Hao Chen (3 shared papers)Yang Xu (1 shared paper)Chao Zhang (2 shared papers)Fangtian Ying (4 shared papers)Cheng Yao (4 shared papers)Baoshan Ma (1 shared paper)
In The Last Decade
Ge Yan
25 papers receiving 461 citations
Ge Yan's Hit Papers
Peers
Comparison fields: 5 of 111
- Computer Science Applications 38
- Human-Computer Interaction 28
- Cancer Research 68
- Computer Vision and Pattern Recognition 68
- Media Technology 23
Countries citing papers authored by Ge Yan
This map shows the geographic impact of Ge Yan'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 Ge Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ge Yan more than expected).
Fields of papers citing papers by Ge Yan
This network shows the impact of papers produced by Ge Yan. 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 Ge Yan. The network helps show where Ge Yan may publish in the future.
Co-authors
The 25 scholars most cited alongside Ge Yan, 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 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Brucea javanica derived exosome-like nanovesicles deliver miRNAs for cancer therapy Hit paper breakdown → | 2024 | 99 |
| 2 | 2022 | 63 | |
| 3 | 2022 | 53 | |
| 4 | 2024 | 48 | |
| 5 | 2021 | 36 | |
| 6 | 2024 | 35 | |
| 7 | 2021 | 32 | |
| 8 | 2022 | 13 | |
| 9 | 2019 | 13 | |
| 10 | 2025 | 11 | |
| 11 | 2023 | 9 | |
| 12 | 2019 | 7 | |
| 13 | 2022 | 7 | |
| 14 | 2021 | 6 | |
| 15 | 2022 | 6 | |
| 16 | 2024 | 6 | |
| 17 | 2019 | 5 | |
| 18 | 2024 | 4 | |
| 19 | 2022 | 4 | |
| 20 | 2023 | 3 |
About Ge Yan
Ge Yan is a scholar working on Molecular Biology, Artificial Intelligence, Cancer Research, Computer Vision and Pattern Recognition and Developmental and Educational Psychology, having authored 28 papers that have together received 468 indexed citations. Recurring topics across this work include Extracellular vesicles in disease (4 papers), MicroRNA in disease regulation (4 papers), Artificial Intelligence in Law (2 papers), RNA Interference and Gene Delivery (2 papers), Visual Attention and Saliency Detection (2 papers), Topic Modeling (2 papers), Advanced Image and Video Retrieval Techniques (2 papers) and Wound Healing and Treatments (2 papers). The work is most often cited by research in Computer Science Applications (38 citations), Human-Computer Interaction (28 citations), Cancer Research (68 citations), Computer Vision and Pattern Recognition (68 citations) and Media Technology (23 citations). Ge Yan has collaborated with scholars based in China, Macao and Sweden. Frequent co-authors include Li‐Hua Peng, Minhong Tan, Hao Chen, Yang Xu, Chao Zhang, Fangtian Ying, Cheng Yao, Baoshan Ma, Lijuan Liu and Xiaoyu Hou. Their work appears in journals such as Frontiers in Cellular and Infection Microbiology, Journal of Research on Technology in Education, Educational Technology Research and Development, Biomaterials Research and Neurocomputing.
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.