Ming Tan

1.6k total citations
62 papers, 1.1k citations indexed

About

Ming Tan is a scholar working on Microbiology, Epidemiology and Molecular Biology. According to data from OpenAlex, Ming Tan has authored 62 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Microbiology, 29 papers in Epidemiology and 19 papers in Molecular Biology. Recurrent topics in Ming Tan's work include Reproductive tract infections research (41 papers), Urinary Tract Infections Management (15 papers) and Reproductive System and Pregnancy (11 papers). Ming Tan is often cited by papers focused on Reproductive tract infections research (41 papers), Urinary Tract Infections Management (15 papers) and Reproductive System and Pregnancy (11 papers). Ming Tan collaborates with scholars based in United States, China and Thailand. Ming Tan's co-authors include Christine Sütterlin, Adam C. Wilson, Johnny Akers, Joanne N. Engel, Chris S. Schaumburg, Eric Cheng, A.J. Shaka, Dennis Kibler, Eike Niehus and Elizabeth Di Russo Case and has published in prestigious journals such as Nature Communications, Biochemical and Biophysical Research Communications and Journal of Bacteriology.

In The Last Decade

Ming Tan

59 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ming Tan United States	21	684	423	400	218	154	62	1.1k
Dagmar Heuer Germany	17	493 0.7×	674 1.6×	663 1.7×	416 1.9×	60 0.4×	35	1.6k
Simon Houston Canada	18	372 0.5×	432 1.0×	175 0.4×	82 0.4×	24 0.2×	33	1.0k
Christiaan van Ooij United States	22	216 0.3×	798 1.9×	305 0.8×	425 1.9×	49 0.3×	57	2.3k
Amy Martin United States	26	240 0.4×	650 1.5×	315 0.8×	167 0.8×	13 0.1×	43	1.7k
M S Urdea United States	23	250 0.4×	984 2.3×	1.1k 2.7×	174 0.8×	112 0.7×	35	2.7k
Mayumi Matsuoka Japan	19	410 0.6×	312 0.7×	645 1.6×	263 1.2×	50 0.3×	38	1.4k
Mahmood Ghassemi United States	14	139 0.2×	313 0.7×	158 0.4×	144 0.7×	29 0.2×	36	821
Osvaldo Reyes Cuba	23	220 0.3×	603 1.4×	99 0.2×	369 1.7×	27 0.2×	91	1.4k
Jean‐Paul Prieels Belgium	19	317 0.5×	895 2.1×	702 1.8×	381 1.7×	116 0.8×	34	2.0k
Deborah L. Diamond United States	22	73 0.1×	766 1.8×	502 1.3×	266 1.2×	52 0.3×	30	1.6k

Countries citing papers authored by Ming Tan

Since Specialization

Citations

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

Fields of papers citing papers by Ming Tan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Tan

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

All Works

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20 of 20 papers shown

Chen, Gaoyang, et al.. (2025). Sensitive detection of miR-21 and miR-25 in gastric adenocarcinoma patient serum using a SERS sensor based on AuNT and enzyme cleavage strategy. RSC Advances. 15(6). 4421–4430.

Xu, Kexin, Na‐Na Tao, Sheng‐Tao Cheng, et al.. (2024). ZNF148 inhibits HBV replication by downregulating RXRα transcription. Virology Journal. 21(1). 35–35. 2 indexed citations

Tan, Ming, et al.. (2024). Chlamydia trachomatis induces disassembly of the primary cilium to promote the intracellular infection. PLoS Pathogens. 20(6). e1012303–e1012303. 1 indexed citations

Tan, Ming, et al.. (2024). Development of an sRNA-mediated conditional knockdown system for Chlamydia trachomatis. mBio. 16(2). e0254524–e0254524.

Ren, Fang, Junchi Hu, Yongjun Dang, et al.. (2023). Sphondin efficiently blocks HBsAg production and cccDNA transcription through promoting HBx degradation. Journal of Medical Virology. 95(3). e28578–e28578. 11 indexed citations

Tan, Ming, et al.. (2022). Differential Effects of Small Molecule Inhibitors on the Intracellular Chlamydia Infection. mBio. 13(4). e0107622–e0107622. 1 indexed citations

Wang, Kevin, et al.. (2022). A Reverse Genetic Approach for Studying sRNAs in Chlamydia trachomatis. mBio. 13(4). e0086422–e0086422. 3 indexed citations

Tan, Ming, et al.. (2022). Chlamydia trachomatis RsbU Phosphatase Activity Is Inhibited by the Enolase Product, Phosphoenolpyruvate. Journal of Bacteriology. 204(10). e0017822–e0017822. 4 indexed citations

Tao, Na‐Na, Ming Tan, Yuan Zhang, et al.. (2022). SIRT2 Promotes HBV Transcription and Replication by Targeting Transcription Factor p53 to Increase the Activities of HBV Enhancers and Promoters. Frontiers in Microbiology. 13. 836446–836446. 17 indexed citations

10.

Wang, Kevin, et al.. (2021). The Small Molecule H89 Inhibits Chlamydia Inclusion Growth and Production of Infectious Progeny. Infection and Immunity. 89(7). e0072920–e0072920. 5 indexed citations

11.

Tan, Ming, et al.. (2021). Anti-HBV therapeutic potential of small molecule 3,5,6,7,3′,4′-Hexamethoxyflavone in vitro and in vivo. Virology. 560. 66–75. 4 indexed citations

12.

Enciso, Germán, Christine Sütterlin, Ming Tan, & Frederic Y. M. Wan. (2021). Stochastic Chlamydia Dynamics and Optimal Spread. Bulletin of Mathematical Biology. 83(4). 24–24. 4 indexed citations

13.

Zhang, Qiang, et al.. (2020). The Repressor Function of the Chlamydia Late Regulator EUO Is Enhanced by the Plasmid-Encoded Protein Pgp4. Journal of Bacteriology. 202(8). 8 indexed citations

14.

Enciso, Germán, Daniela Boassa, Frederic Y. M. Wan, et al.. (2017). Replication-dependent size reduction precedes differentiation in Chlamydia trachomatis. Nature Communications. 9(1). 45–45. 72 indexed citations

15.

Tan, Ming, et al.. (2015). Functional analysis of three topoisomerases that regulate DNA supercoiling levels in Chlamydia. Molecular Microbiology. 99(3). 484–496. 8 indexed citations

16.

Sütterlin, Christine, et al.. (2012). CPAF: A Chlamydial Protease in Search of an Authentic Substrate. PLoS Pathogens. 8(8). e1002842–e1002842. 97 indexed citations

17.

Hasegawa, Ayako, et al.. (2009). Host Complement Regulatory Protein CD59 Is Transported to the Chlamydial Inclusion by a Golgi Apparatus-Independent Pathway. Infection and Immunity. 77(4). 1285–1292. 9 indexed citations

18.

Tan, Ming, et al.. (2009). Centrosome abnormalities during aChlamydia trachomatisinfection are caused by dysregulation of the normal duplication pathway. Cellular Microbiology. 11(7). 1064–1073. 35 indexed citations

19.

Niehus, Eike, Eric Cheng, & Ming Tan. (2008). DNA Supercoiling-Dependent Gene Regulation in Chlamydia. Journal of Bacteriology. 190(19). 6419–6427. 29 indexed citations

20.

Yamanaka, K, Pete Chandrangsu, Johnny Akers, et al.. (2008). The cell-penetrating peptide, Pep-1, has activity against intracellular chlamydial growth but not extracellular forms of Chlamydia trachomatis. Journal of Antimicrobial Chemotherapy. 63(1). 115–123. 23 indexed citations

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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