Fang Nan

33 total papers · 1.5k total citations
18 papers, 1.0k citations indexed

About

Fang Nan is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Fang Nan has authored 18 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 6 papers in Immunology and 4 papers in Oncology. Recurrent topics in Fang Nan's work include T-cell and B-cell Immunology (3 papers), Enzyme Production and Characterization (3 papers) and Peptidase Inhibition and Analysis (2 papers). Fang Nan is often cited by papers focused on T-cell and B-cell Immunology (3 papers), Enzyme Production and Characterization (3 papers) and Peptidase Inhibition and Analysis (2 papers). Fang Nan collaborates with scholars based in China, United States and Japan. Fang Nan's co-authors include Chu‐Xiao Liu, Li Yang, Ling‐Ling Chen, Si-Kun Guo, Bo Qu, Wei Xue, Xiang Li, Yange Cui, Zhaocai Zhou and Shan Jiang and has published in prestigious journals such as Cell, Nature Communications and Journal of Hazardous Materials.

In The Last Decade

Fang Nan

18 papers receiving 1.0k citations

Hit Papers

Structure and Degradation... 2019 2026 2021 2023 2019 2022 100 200 300 400 500

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Fang Nan 716 462 108 76 56 18 1.0k
Lei Shi 824 1.2× 202 0.4× 68 0.6× 155 2.0× 23 0.4× 19 1.2k
Lingying Tong 681 1.0× 422 0.9× 97 0.9× 80 1.1× 28 0.5× 24 1.2k
Bo Xu 832 1.2× 392 0.8× 62 0.6× 120 1.6× 27 0.5× 39 1.2k
Xi Li 436 0.6× 194 0.4× 101 0.9× 120 1.6× 41 0.7× 27 1.1k
Zhibin Wang 599 0.8× 320 0.7× 121 1.1× 177 2.3× 16 0.3× 19 1.1k
Rossella Puglisi 686 1.0× 323 0.7× 174 1.6× 38 0.5× 15 0.3× 34 1.2k
Srinivas Patnaik 701 1.0× 190 0.4× 78 0.7× 69 0.9× 19 0.3× 41 1.2k
Vindhya Koppaka 517 0.7× 232 0.5× 52 0.5× 72 0.9× 30 0.5× 9 1.2k
Salvatore Salzano 483 0.7× 219 0.5× 82 0.8× 129 1.7× 13 0.2× 25 1.2k
Geraldine Paszkiewicz 537 0.8× 140 0.3× 64 0.6× 198 2.6× 20 0.4× 24 1.2k

Countries citing papers authored by Fang Nan

Since Specialization
Citations

This map shows the geographic impact of Fang Nan'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 Nan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang Nan more than expected).

Fields of papers citing papers by Fang Nan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fang Nan. 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 Nan. The network helps show where Fang Nan may publish in the future.

Co-authorship network of co-authors of Fang Nan

This figure shows the co-authorship network connecting the top 25 collaborators of Fang Nan. A scholar is included among the top collaborators of Fang Nan 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 Nan. Fang Nan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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