Ming Yan

46 total papers · 1.2k total citations
35 papers, 398 citations indexed

About

Ming Yan is a scholar working on Molecular Biology, Physiology and Pathology and Forensic Medicine. According to data from OpenAlex, Ming Yan has authored 35 papers receiving a total of 398 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 7 papers in Physiology and 6 papers in Pathology and Forensic Medicine. Recurrent topics in Ming Yan's work include Liver physiology and pathology (5 papers), Alcohol Consumption and Health Effects (4 papers) and Liver Disease Diagnosis and Treatment (4 papers). Ming Yan is often cited by papers focused on Liver physiology and pathology (5 papers), Alcohol Consumption and Health Effects (4 papers) and Liver Disease Diagnosis and Treatment (4 papers). Ming Yan collaborates with scholars based in China, Australia and United States. Ming Yan's co-authors include Chen Chen, Lei Peng, Zheng Luo, Dongling Liu, María Isabel Hernández, Margaret E. E. Jones, Jianjian Zhao, Evan R. Simpson, Xiaolan Guo and Yan Yang and has published in prestigious journals such as PLoS ONE, Scientific Reports and Biochemical and Biophysical Research Communications.

In The Last Decade

Ming Yan

33 papers receiving 392 citations

Author Peers

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

Author Last Decade Papers Cites
Ming Yan 93 90 77 72 61 35 398
T. N. Rasmussen 130 1.4× 46 0.5× 45 0.6× 32 0.4× 21 0.3× 24 384
Shuiqing Lai 60 0.6× 58 0.6× 138 1.8× 30 0.4× 12 0.2× 23 333
Shohei Tsuji 96 1.0× 78 0.9× 28 0.4× 13 0.2× 74 1.2× 31 344
Yuya Nakamura 50 0.5× 40 0.4× 120 1.6× 45 0.6× 7 0.1× 31 332
Fan Yang 97 1.0× 23 0.3× 46 0.6× 17 0.2× 18 0.3× 30 363
Marı́a José de la Vega-Prieto 100 1.1× 158 1.8× 46 0.6× 11 0.2× 69 1.1× 26 438
Takashi Kano 89 1.0× 21 0.2× 32 0.4× 22 0.3× 27 0.4× 35 335
Imran J. Anwar 48 0.5× 29 0.3× 79 1.0× 25 0.3× 34 0.6× 32 354
Tomoko Nawata 191 2.1× 45 0.5× 35 0.5× 39 0.5× 7 0.1× 20 445
A Brocchi 113 1.2× 113 1.3× 98 1.3× 20 0.3× 20 0.3× 25 436

Countries citing papers authored by Ming Yan

Since Specialization
Citations

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

Fields of papers citing papers by Ming Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Yan. A scholar is included among the top collaborators of Ming Yan 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 Ming Yan. Ming Yan 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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