Michael Q. Zhang

6.2k total citations · 2 hit papers
49 papers, 4.0k citations indexed

About

Michael Q. Zhang is a scholar working on Molecular Biology, Cancer Research and Ocean Engineering. According to data from OpenAlex, Michael Q. Zhang has authored 49 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 19 papers in Cancer Research and 3 papers in Ocean Engineering. Recurrent topics in Michael Q. Zhang's work include Cancer-related molecular mechanisms research (13 papers), RNA modifications and cancer (13 papers) and MicroRNA in disease regulation (11 papers). Michael Q. Zhang is often cited by papers focused on Cancer-related molecular mechanisms research (13 papers), RNA modifications and cancer (13 papers) and MicroRNA in disease regulation (11 papers). Michael Q. Zhang collaborates with scholars based in United States, China and France. Michael Q. Zhang's co-authors include Zhenyu Xuan, Gregory J. Hannon, Michelle A. Carmell, Adrian R. Krainer, Luca Cartegni, Hong‐Xiang Liu, Andrew D. Smith, Hirotoshi Kikuchi, Daniel C. Chung and Ramnik J. Xavier and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Michael Q. Zhang

47 papers receiving 4.0k citations

Hit Papers

The Argonaute family: tentacles that reach into RNAi, dev... 2002 2026 2010 2018 2002 2010 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Q. Zhang United States 25 3.1k 1.5k 435 424 272 49 4.0k
Hairi Li United States 31 4.6k 1.5× 964 0.6× 327 0.8× 728 1.7× 218 0.8× 43 5.9k
Claudio Santoro Italy 31 3.1k 1.0× 1.2k 0.8× 428 1.0× 185 0.4× 358 1.3× 90 4.1k
Oriol Fornés Canada 17 2.9k 0.9× 656 0.4× 540 1.2× 392 0.9× 389 1.4× 34 3.8k
Ran Elkon Israel 39 4.9k 1.6× 1.3k 0.9× 346 0.8× 302 0.7× 320 1.2× 81 5.7k
Irina Khrebtukova United States 20 6.0k 1.9× 1.6k 1.1× 548 1.3× 309 0.7× 465 1.7× 31 6.9k
Maxwell R. Mumbach United States 20 4.6k 1.5× 1.5k 1.0× 343 0.8× 487 1.1× 358 1.3× 27 5.2k
Hervé Pagès United States 7 3.1k 1.0× 635 0.4× 660 1.5× 599 1.4× 455 1.7× 10 4.2k
Guang Hu United States 34 4.3k 1.4× 724 0.5× 549 1.3× 455 1.1× 424 1.6× 94 5.1k
Charles E. Vejnar United States 23 3.0k 1.0× 1.1k 0.7× 416 1.0× 274 0.6× 242 0.9× 33 3.6k
Darren A. Cusanovich United States 18 4.1k 1.3× 818 0.5× 491 1.1× 236 0.6× 528 1.9× 28 4.7k

Countries citing papers authored by Michael Q. Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Michael Q. Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Q. Zhang. 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 Michael Q. Zhang. The network helps show where Michael Q. Zhang may publish in the future.

Co-authorship network of co-authors of Michael Q. Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Q. Zhang. A scholar is included among the top collaborators of Michael Q. Zhang 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 Michael Q. Zhang. Michael Q. Zhang 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.
Jia, Chen, Hong Qian, & Michael Q. Zhang. (2024). Exact Power Spectrum in a Minimal Hybrid Model of Stochastic Gene Expression Oscillations. SIAM Journal on Applied Mathematics. 84(3). 1204–1226. 3 indexed citations
2.
Zhang, Michael Q.. (2023). Dialog between artificial intelligence & natural intelligence. Quantitative Biology. 11(4). 359–362.
4.
Su, Xiaonan, Ji‐Eun Lee, Yutong Song, et al.. (2020). Molecular basis for histone H3 “K4me3-K9me3/2” methylation pattern readout by Spindlin1. Journal of Biological Chemistry. 295(49). 16877–16887. 18 indexed citations
5.
Li, Jing, Younghee Lee, Yu Jiang, et al.. (2018). Co-inhibitory Molecule B7 Superfamily Member 1 Expressed by Tumor-Infiltrating Myeloid Cells Induces Dysfunction of Anti-tumor CD8+ T Cells. Immunity. 48(4). 773–786.e5. 165 indexed citations
6.
Gu, Jin, et al.. (2014). Gene module based regulator inference identifying miR-139 as a tumor suppressor in colorectal cancer. Molecular BioSystems. 10(12). 3249–3254. 11 indexed citations
7.
Liao, Will, Weiming Ouyang, Michael Q. Zhang, & Ming O. Li. (2014). Genome wide mapping of Foxo1 binding-sites in murine T lymphocytes. Genomics Data. 2. 280–281. 5 indexed citations
8.
Hu, Long, Chao Di, Yucheng Yang, et al.. (2014). A common set of distinct features that characterize noncoding RNAs across multiple species. Nucleic Acids Research. 43(1). 104–114. 29 indexed citations
9.
Wu, Jie, Olga Anczuków, Adrian R. Krainer, Michael Q. Zhang, & Chaolin Zhang. (2013). OLego: fast and sensitive mapping of spliced mRNA-Seq reads using small seeds. Nucleic Acids Research. 41(10). 5149–5163. 91 indexed citations
10.
Guo, Weilong, Petko Fiziev, Weihong Yan, et al.. (2013). BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data. BMC Genomics. 14(1). 774–774. 281 indexed citations
11.
Li, Ruijuan, Weilong Guo, Jin Gu, Michael Q. Zhang, & Xiaowo Wang. (2012). Chromatin state and microRNA determine different gene expression dynamics responsive to TNF stimulation. Genomics. 100(5). 297–302. 4 indexed citations
12.
He, Miao, Yu Liu, Xiaowo Wang, et al.. (2012). Cell-Type-Based Analysis of MicroRNA Profiles in the Mouse Brain. Neuron. 73(1). 35–48. 230 indexed citations
13.
Bernard, Delphine, Kannanganattu V. Prasanth, Vidisha Tripathi, et al.. (2010). A long nuclear‐retained non‐coding RNA regulates synaptogenesis by modulating gene expression. The EMBO Journal. 29(18). 3082–3093. 595 indexed citations breakdown →
14.
Benita, Yair, Hirotoshi Kikuchi, Andrew D. Smith, et al.. (2009). An integrative genomics approach identifies Hypoxia Inducible Factor-1 (HIF-1)-target genes that form the core response to hypoxia. Nucleic Acids Research. 37(14). 4587–4602. 361 indexed citations
15.
Hodges, Emily, Andrew D. Smith, Jude Kendall, et al.. (2009). High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing. Genome Research. 19(9). 1593–1605. 175 indexed citations
16.
Das, Debopriya, et al.. (2008). Regulation of the PDK4 Isozyme by the Rb-E2F1 Complex. Journal of Biological Chemistry. 283(41). 27410–27417. 90 indexed citations
17.
Zhang, Hua, Zhenhua Li, Michael Q. Zhang, Michael Katz, & Bin-Xian Zhang. (2008). Heat Shock Protein 90β1 Is Essential for Polyunsaturated Fatty Acid-induced Mitochondrial Ca2+ Efflux. Journal of Biological Chemistry. 283(12). 7580–7589. 8 indexed citations
18.
Wang, Xiaowo, Jin Gu, Michael Q. Zhang, & Yanda Li. (2007). Identification of phylogenetically conserved microRNA cis-regulatory elements across 12 Drosophila species. Bioinformatics. 24(2). 165–171. 12 indexed citations
19.
Jiang, Tao, Ying Xu, & Michael Q. Zhang. (2002). Computational Methods for Promoter Recognition. 249–267. 10 indexed citations
20.
Zhang, Michael Q., et al.. (1994). Mechanisms of Heliothis virescens resistance to exogenous ecdysteroids.. 182–201. 2 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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