Guan-nan Jin

27 total papers · 862 total citations
23 papers, 530 citations indexed

About

Guan-nan Jin is a scholar working on Molecular Biology, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Guan-nan Jin has authored 23 papers receiving a total of 530 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 7 papers in Oncology and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Guan-nan Jin's work include TGF-β signaling in diseases (6 papers), Liver physiology and pathology (5 papers) and COVID-19 Clinical Research Studies (3 papers). Guan-nan Jin is often cited by papers focused on TGF-β signaling in diseases (6 papers), Liver physiology and pathology (5 papers) and COVID-19 Clinical Research Studies (3 papers). Guan-nan Jin collaborates with scholars based in China, United States and United Kingdom. Guan-nan Jin's co-authors include Zeyang Ding, Bixiang Zhang, Ming‐Zhi Zhang, Xiaoping Chen, Weixun Chen, Pran K. Datta, Huifang Liang, Suwan Wang, Yinqiu Wang and Xiaofeng Fan and has published in prestigious journals such as Nature Communications, Hepatology and International Journal of Molecular Sciences.

In The Last Decade

Guan-nan Jin

22 papers receiving 526 citations

Author Peers

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

Author Last Decade Papers Cites
Guan-nan Jin 268 113 85 81 76 23 530
Federico Pio Fabrizio 211 0.8× 116 1.0× 76 0.9× 72 0.9× 124 1.6× 32 463
Sonia Chothani 263 1.0× 90 0.8× 52 0.6× 80 1.0× 100 1.3× 22 568
Natsumi Sakamoto 209 0.8× 108 1.0× 73 0.9× 35 0.4× 72 0.9× 27 569
Xiaoqing Jia 378 1.4× 125 1.1× 173 2.0× 77 1.0× 97 1.3× 36 586
Jie Wen 124 0.5× 98 0.9× 67 0.8× 59 0.7× 101 1.3× 26 598
Kenneth Yanek 241 0.9× 72 0.6× 102 1.2× 67 0.8× 127 1.7× 13 573
Kathleen Woodruff 335 1.3× 152 1.3× 92 1.1× 49 0.6× 43 0.6× 20 575
Yiju Cheng 193 0.7× 64 0.6× 68 0.8× 110 1.4× 116 1.5× 20 518
Bhaskar Kallakury 177 0.7× 105 0.9× 85 1.0× 52 0.6× 69 0.9× 32 521
Homare Ito 237 0.9× 121 1.1× 138 1.6× 187 2.3× 72 0.9× 25 516

Countries citing papers authored by Guan-nan Jin

Since Specialization
Citations

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

Fields of papers citing papers by Guan-nan Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guan-nan 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 Guan-nan Jin. The network helps show where Guan-nan Jin may publish in the future.

Co-authorship network of co-authors of Guan-nan Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Guan-nan Jin. A scholar is included among the top collaborators of Guan-nan 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 Guan-nan Jin. Guan-nan Jin 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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