Fang Jin
- Sociology and Political Science top 5%
- Artificial Intelligence top 5%
- Molecular Biology
- Statistical and Nonlinear Physics top 5%
- Epidemiology
- Co-authors
- Naren RamakrishnanEdward J. DoughertyYang CaoParang SarafYu‐Ming ChuMati ur RahmanGuofeng CaoPál L. Vághy
- Topics
- Extracellular vesicles in disease (6 papers)Adversarial Robustness in Machine Learning (5 papers)Nanoplatforms for cancer theranostics (5 papers)
- Journals
- Angewandte Chemie International EditionSHILAP Revista de lepidopterologíaThe Journal of Immunology
- Partner nations
- ChinaUnited StatesTaiwan
In The Last Decade
Fang Jin
138 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 176
- Sociology and Political Science 351
- Artificial Intelligence 309
- Molecular Biology 303
- Statistical and Nonlinear Physics 201
- Epidemiology 194
Countries citing papers authored by Fang Jin
This map shows the geographic impact of Fang Jin'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 Fang Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang Jin more than expected).
Fields of papers citing papers by Fang Jin
This network shows the impact of papers produced by Fang Jin. 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 Fang Jin. The network helps show where Fang Jin may publish in the future.
Co-authorship network of co-authors of Fang Jin
This figure shows the co-authorship network connecting the top 25 collaborators of Fang Jin. A scholar is included among the top collaborators of Fang Jin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fang Jin. Fang Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 5 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 3 | |
| 6 | 6 | |
| 7 | 0 | |
| 8 | 5 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 130 | |
| 12 | 28 | |
| 13 | 1 | |
| 14 | The development of MOOC and its challenge to traditional education | 2 |
| 15 | Research on the Stochastic Hybrid Multi-attribute Decision Making Method Based on Prospect Theory | 14 |
| 16 | Nursing Experience of High Internal Arteriovenous Fistula for Patients with Chronic Hemodialysis | 0 |
| 17 | Preparation and in Vivo Biotransformation of Curcumin Micelles | 2 |
| 18 | 7 | |
| 19 | Research About Visualization of Hybrid Multi-resolution Terrain and Vector Data | 2 |
| 20 | Soft-Decision Viterbi and Sequential Decoding on FPGA | 1 |
About Fang Jin
Fang Jin is a scholar working on Communication, Artificial Intelligence and Genetics, having authored 152 papers that have together received 1.8k indexed citations. Recurring topics across this work include Extracellular vesicles in disease (6 papers), Adversarial Robustness in Machine Learning (5 papers) and Nanoplatforms for cancer theranostics (5 papers). The work is most often cited by research in Communication (147 citations), Statistical and Nonlinear Physics (201 citations) and Modeling and Simulation (60 citations). Fang Jin has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Naren Ramakrishnan, Edward J. Dougherty, Yang Cao, Parang Saraf, Yu‐Ming Chu, Mati ur Rahman, Guofeng Cao, Pál L. Vághy, Souparno Ghosh and Ali Nejat. Their work appears in journals such as Angewandte Chemie International Edition, SHILAP Revista de lepidopterología and The Journal of Immunology.
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.