Michelle Tang

2.3k total citations
25 papers, 1.1k citations indexed

About

Michelle Tang is a scholar working on Molecular Biology, Plant Science and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Michelle Tang has authored 25 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 11 papers in Plant Science and 3 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Michelle Tang's work include Plant Molecular Biology Research (8 papers), Plant Stress Responses and Tolerance (4 papers) and Photosynthetic Processes and Mechanisms (3 papers). Michelle Tang is often cited by papers focused on Plant Molecular Biology Research (8 papers), Plant Stress Responses and Tolerance (4 papers) and Photosynthetic Processes and Mechanisms (3 papers). Michelle Tang collaborates with scholars based in United States, Denmark and China. Michelle Tang's co-authors include Daniel J. Kliebenstein, Baohua Li, Allison Gaudinier, Siobhán M. Brady, Mohammad Salehin, Liang Song, Joseph R. Ecker, Mark Estelle, Ella Katz and J. Chris Pires and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Michelle Tang

23 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michelle Tang United States 15 701 564 184 110 81 25 1.1k
Tatjana Sjakste Latvia 17 912 1.3× 488 0.9× 423 2.3× 50 0.5× 40 0.5× 59 1.4k
Dhinoth Bangarusamy United States 15 327 0.5× 713 1.3× 199 1.1× 59 0.5× 17 0.2× 23 1.1k
Marion Wood United Kingdom 14 1.1k 1.6× 921 1.6× 83 0.5× 115 1.0× 44 0.5× 22 1.8k
Ze Peng United States 19 696 1.0× 465 0.8× 97 0.5× 149 1.4× 23 0.3× 61 1.2k
Robert J. Anderberg United States 14 737 1.1× 452 0.8× 49 0.3× 63 0.6× 14 0.2× 15 1.1k
Tie Liu United States 21 1.1k 1.5× 951 1.7× 88 0.5× 62 0.6× 51 0.6× 64 1.6k
Jingjie Zhu China 15 737 1.1× 567 1.0× 393 2.1× 57 0.5× 30 0.4× 37 1.2k
Tomohiko Kubo Japan 22 898 1.3× 1.5k 2.6× 289 1.6× 25 0.2× 218 2.7× 64 2.0k

Countries citing papers authored by Michelle Tang

Since Specialization
Citations

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

Fields of papers citing papers by Michelle Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michelle Tang

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

All Works

20 of 20 papers shown
1.
Tang, Michelle, Gareth A. Cromie, Russell S. Lo, et al.. (2026). Predicting epistasis across proteins by structural logic. Proceedings of the National Academy of Sciences. 123(3). e2516291123–e2516291123.
2.
Wang, Huihui, Huiyuan Wang, Hongwei Wu, et al.. (2025). Transcriptome-wide m6A modification and poly(A) tail length changes in moso bamboo induced by gibberellin revealed by nanopore direct RNA sequencing. Industrial Crops and Products. 225. 120549–120549. 3 indexed citations
3.
4.
Lo, Russell S., Gareth A. Cromie, Michelle Tang, et al.. (2023). The functional impact of 1,570 individual amino acid substitutions in human OTC. The American Journal of Human Genetics. 110(5). 863–879. 10 indexed citations
5.
Cromie, Gareth A., Katherine Owens, Michelle Tang, et al.. (2023). Constructing and interpreting a large-scale variant effect map for an ultrarare disease gene: Comprehensive prediction of the functional impact of PSAT1 genotypes. PLoS Genetics. 19(10). e1010972–e1010972. 1 indexed citations
6.
7.
Tang, Michelle, Baohua Li, Jia Jie Li, et al.. (2021). A genome‐scale TF–DNA interaction network of transcriptional regulation of Arabidopsis primary and specialized metabolism. Molecular Systems Biology. 17(11). e10625–e10625. 27 indexed citations
8.
Fernández‐Calvo, Patricia, Sabrina Iñigo, Gaétan Glauser, et al.. (2020). FRS7 and FRS12 recruit NINJA to regulate expression of glucosinolate biosynthesis genes. New Phytologist. 227(4). 1124–1137. 14 indexed citations
9.
Li, Baohua, et al.. (2019). Epistatic Transcription Factor Networks Differentially Modulate Arabidopsis Growth and Defense. Genetics. 214(2). 529–541. 15 indexed citations
10.
Salehin, Mohammad, Baohua Li, Michelle Tang, et al.. (2019). Auxin-sensitive Aux/IAA proteins mediate drought tolerance in Arabidopsis by regulating glucosinolate levels. Nature Communications. 10(1). 4021–4021. 220 indexed citations
11.
Wood, Amber, et al.. (2019). Vaginal Mycoplasmataceae colonization and association with immune mediators in pregnancy. The Journal of Maternal-Fetal & Neonatal Medicine. 34(14). 2295–2302. 5 indexed citations
12.
Gaudinier, Allison, Joel Rodríguez-Medina, Lifang Zhang, et al.. (2018). Transcriptional regulation of nitrogen-associated metabolism and growth. Nature. 563(7730). 259–264. 253 indexed citations
13.
Gaudinier, Allison, Michelle Tang, Anne-Maarit Bågman, & Siobhán M. Brady. (2017). Identification of Protein–DNA Interactions Using Enhanced Yeast One-Hybrid Assays and a Semiautomated Approach. Methods in molecular biology. 1610. 187–215. 13 indexed citations
14.
Gaudinier, Allison, Michelle Tang, & Daniel J. Kliebenstein. (2015). Transcriptional networks governing plant metabolism. Current Plant Biology. 3-4. 56–64. 26 indexed citations
15.
Edger, Patrick P., Michelle Tang, Kevin A. Bird, et al.. (2014). Secondary Structure Analyses of the Nuclear rRNA Internal Transcribed Spacers and Assessment of Its Phylogenetic Utility across the Brassicaceae (Mustards). PLoS ONE. 9(7). e101341–e101341. 25 indexed citations
16.
Arias, Tatiana, Mark A. Beilstein, Michelle Tang, Michael R. McKain, & J. Chris Pires. (2014). Diversification times among Brassica (Brassicaceae) crops suggest hybrid formation after 20 million years of divergence. American Journal of Botany. 101(1). 86–91. 70 indexed citations
17.
Li, Maoyin, et al.. (2014). Overexpression of patatin‐related phospholipase AIIIδ altered plant growth and increased seed oil content in camelina. Plant Biotechnology Journal. 13(6). 766–778. 50 indexed citations
18.
Liewlaksaneeyanawin, Cherdsak, Jun Zhuang, Michelle Tang, et al.. (2008). Identification of COS markers in the Pinaceae. Tree Genetics & Genomes. 5(1). 247–255. 21 indexed citations
19.
Hu, Zhaozheng, Michelle Tang, & H.T. Tsui. (2002). Direct triangle extraction by a randomized Hough technique. 1. 717–719. 3 indexed citations
20.
Tang, Michelle, et al.. (1999). Modification of β<sub>2</sub>glycoprotein I by Glutardialdehyde: Conformational Changes and Aggregation Accompany Exposure of the Cryptic Autoepitope. Applied Biochemistry and Biotechnology. 76(1). 1–14. 15 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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