Michael Su

10.4k total citations · 4 hit papers
50 papers, 8.8k citations indexed

About

Michael Su is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Michael Su has authored 50 papers receiving a total of 8.8k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 11 papers in Immunology and 10 papers in Oncology. Recurrent topics in Michael Su's work include Cell death mechanisms and regulation (9 papers), Melanoma and MAPK Pathways (8 papers) and Acute Myeloid Leukemia Research (7 papers). Michael Su is often cited by papers focused on Cell death mechanisms and regulation (9 papers), Melanoma and MAPK Pathways (8 papers) and Acute Myeloid Leukemia Research (7 papers). Michael Su collaborates with scholars based in United States, Japan and France. Michael Su's co-authors include Keisuke Kuida, Richard A. Flavell, Yong Gu, David J. Livingston, Matthew W. Harding, George Ku, Judith A. Lippke, Pasko Rakić, Tarik F. Haydar and Chia‐Yi Kuan and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Michael Su

49 papers receiving 8.6k citations

Hit Papers

Altered Cytokine Export and Apoptosis in Mice Deficient i... 1995 2026 2005 2015 1995 1998 1997 2004 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Su United States 32 6.4k 2.6k 1.4k 1.1k 884 50 8.8k
Christian Rommel United States 44 7.3k 1.1× 2.1k 0.8× 1.7k 1.2× 1.0k 0.9× 516 0.6× 88 10.2k
Joseph T. Opferman United States 43 6.0k 0.9× 2.6k 1.0× 1.5k 1.0× 1.2k 1.1× 1.9k 2.2× 95 9.5k
Hamsa Puthalakath Australia 32 4.8k 0.8× 2.0k 0.8× 1.2k 0.9× 1.5k 1.3× 1.2k 1.3× 65 7.2k
Silvano Capitani Italy 51 4.8k 0.7× 1.6k 0.6× 1.3k 0.9× 813 0.7× 737 0.8× 262 7.9k
Paul G. Ekert Australia 41 5.9k 0.9× 1.7k 0.6× 1.3k 0.9× 620 0.5× 1.3k 1.4× 128 8.3k
Raymond B. Birge United States 47 3.6k 0.6× 3.2k 1.2× 1.1k 0.8× 1.0k 0.9× 502 0.6× 112 7.6k
Tak W. Mak Canada 32 5.6k 0.9× 2.3k 0.9× 1.3k 0.9× 810 0.7× 859 1.0× 44 8.1k
Andreas Villunger Austria 55 7.5k 1.2× 2.8k 1.1× 3.1k 2.1× 1.2k 1.0× 1.0k 1.1× 183 10.7k
Roberto Testi Italy 47 4.2k 0.7× 3.0k 1.1× 795 0.6× 704 0.6× 600 0.7× 113 7.4k
Xosé R. Bustelo Spain 57 7.0k 1.1× 2.8k 1.1× 2.0k 1.4× 1.9k 1.7× 382 0.4× 172 11.1k

Countries citing papers authored by Michael Su

Since Specialization
Citations

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

Fields of papers citing papers by Michael Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Su

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Su. A scholar is included among the top collaborators of Michael Su 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 Su. Michael Su 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.
Liu, Kevin X., et al.. (2025). NETosis-specific cell death: a novel mechanism in the pathogenesis of gouty arthritis. European journal of medical research. 30(1). 1134–1134.
2.
Geoerger, Birgit, Manuel Schiff, Virginie Penard‐Lacronique, et al.. (2023). Enasidenib treatment in two individuals with D-2-hydroxyglutaric aciduria carrying a germline IDH2 mutation. Nature Medicine. 29(6). 1358–1363. 5 indexed citations
3.
Su, Michael, Xuejun Zhu, & Wenjun Zhang. (2018). Probing the acyl carrier protein–Enzyme interactions within terminal alkyne biosynthetic machinery. AIChE Journal. 64(12). 4255–4262. 10 indexed citations
4.
Zhu, Xuejun, Peyton Shieh, Michael Su, Carolyn R. Bertozzi, & Wenjun Zhang. (2016). A fluorogenic screening platform enables directed evolution of an alkyne biosynthetic tool. Chemical Communications. 52(75). 11239–11242. 16 indexed citations
5.
Wang, Fang, Jeremy Travins, Yue Chen, et al.. (2014). Abstract 3116: AG-221 offers a survival advantage in a primary human IDH2 mutant AML xenograft model. Cancer Research. 74(19_Supplement). 3116–3116. 6 indexed citations
7.
Davis, Mindy I., Rajan Pragani, Janeta Popovici-Müller, et al.. (2013). ML309: A potent inhibitor of R132H mutant IDH1 capable of reducing 2-hydroxyglutarate production in U87 MG glioblastoma cells. Europe PMC (PubMed Central). 16 indexed citations
8.
Yen, Katharine, René M. Lemieux, Janeta Popovici-Müller, et al.. (2013). IDH1 Mutant Inhibitor Induces Cellular Differentiation and Offers a Combination Benefit With Ara-C In a Primary Human Idh1 Mutant AML Xenograft Model. Blood. 122(21). 3946–3946. 13 indexed citations
9.
Harrington, Elizabeth A., David Bebbington, Jeffrey R. Moore, et al.. (2004). VX-680, a potent and selective small-molecule inhibitor of the Aurora kinases, suppresses tumor growth in vivo. Nature Medicine. 10(3). 262–267. 782 indexed citations breakdown →
10.
Ramanathan, Mathura P., et al.. (2002). Carboxyl Terminus of hVIP/mov34 Is Critical for HIV-1-Vpr Interaction and Glucocorticoid-mediated Signaling. Journal of Biological Chemistry. 277(49). 47854–47860. 23 indexed citations
11.
Fox, Ted, et al.. (1999). Kinetic mechanism and ATP‐binding site reactivity of p38γ MAP kinase. FEBS Letters. 461(3). 323–328. 25 indexed citations
12.
Kuida, Keisuke, Tarik F. Haydar, Chia‐Yi Kuan, et al.. (1998). Reduced Apoptosis and Cytochrome c–Mediated Caspase Activation in Mice Lacking Caspase 9. Cell. 94(3). 325–337. 1369 indexed citations breakdown →
13.
Lin, Chien‐Ju, et al.. (1997). The hepatitis C virus NS4A protein: interactions with the NS4B and NS5A proteins. Journal of Virology. 71(9). 6465–6471. 86 indexed citations
14.
Wilson, Keith P., Patricia G. McCaffrey, Kathy Hsiao, et al.. (1997). The structural basis for the specificity of pyridinylimidazole inhibitors of p38 MAP kinase. Chemistry & Biology. 4(6). 423–431. 239 indexed citations
15.
Akita, Kenji, Takashi Ohtsuki, Tadao Tanimoto, et al.. (1997). Involvement of Caspase-1 and Caspase-3 in the Production and Processing of Mature Human Interleukin 18 in Monocytic THP.1 Cells. Journal of Biological Chemistry. 272(42). 26595–26603. 179 indexed citations
16.
Lippke, Judith A., et al.. (1996). Identification and Characterization of CPP32/Mch2 Homolog 1, a Novel Cysteine Protease Similar to CPP32. Journal of Biological Chemistry. 271(4). 1825–1828. 203 indexed citations
17.
Wilson, Keith P., Matthew J. Fitzgibbon, Paul R. Caron, et al.. (1996). Crystal Structure of p38 Mitogen-activated Protein Kinase. Journal of Biological Chemistry. 271(44). 27696–27700. 202 indexed citations
18.
Kawamura, Akinori & Michael Su. (1995). Interaction of FKBP12-FK506 with Calcineurin A at the B Subunit-binding Domain. Journal of Biological Chemistry. 270(26). 15463–15466. 41 indexed citations
19.
Gu, Yong, et al.. (1995). Cleavage of Poly(ADP-ribose) Polymerase by Interleukin-1β Converting Enzyme and Its Homologs TX and Nedd-2. Journal of Biological Chemistry. 270(32). 18715–18718. 145 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026