Michael Shi

8.9k total citations
63 papers, 2.7k citations indexed

About

Michael Shi is a scholar working on Molecular Biology, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Michael Shi has authored 63 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Molecular Biology, 19 papers in Oncology and 18 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Michael Shi's work include Fibroblast Growth Factor Research (10 papers), Cancer Genomics and Diagnostics (7 papers) and Lung Cancer Treatments and Mutations (7 papers). Michael Shi is often cited by papers focused on Fibroblast Growth Factor Research (10 papers), Cancer Genomics and Diagnostics (7 papers) and Lung Cancer Treatments and Mutations (7 papers). Michael Shi collaborates with scholars based in United States, China and Switzerland. Michael Shi's co-authors include Henry Jay Forman, Takeo Iwamoto, Amir Kugelman, Li Tian, Peter A. Ward, Ren-Feng Guo, Andrea Kay, Run Liu, Evelyne Gozal and Henry A. Choy and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Michael Shi

60 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Shi United States 25 1.5k 475 389 384 296 63 2.7k
Peter Ellinghaus Germany 28 1.6k 1.1× 444 0.9× 544 1.4× 380 1.0× 217 0.7× 74 3.1k
Li‐Shun Wang China 30 1.8k 1.2× 487 1.0× 513 1.3× 220 0.6× 243 0.8× 113 2.8k
Paul Nicklin United Kingdom 23 1.8k 1.2× 391 0.8× 516 1.3× 688 1.8× 282 1.0× 40 3.3k
Sinéad Toomey Ireland 19 714 0.5× 494 1.0× 243 0.6× 253 0.7× 231 0.8× 66 2.0k
Barbara Sitek Germany 33 1.6k 1.0× 482 1.0× 464 1.2× 260 0.7× 331 1.1× 125 3.2k
Mahrukh K. Ganapathi United States 27 1.3k 0.9× 590 1.2× 320 0.8× 309 0.8× 374 1.3× 67 2.4k
Laurent Corcos France 37 2.0k 1.3× 951 2.0× 559 1.4× 390 1.0× 398 1.3× 105 4.8k
Yingli Wu China 32 2.2k 1.5× 516 1.1× 541 1.4× 213 0.6× 278 0.9× 145 3.2k
Jochen G. Schneider Germany 28 1.5k 1.0× 375 0.8× 399 1.0× 139 0.4× 293 1.0× 85 3.2k

Countries citing papers authored by Michael Shi

Since Specialization
Citations

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

Fields of papers citing papers by Michael Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Shi. A scholar is included among the top collaborators of Michael Shi 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 Shi. Michael Shi 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
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Zhao, Ning, Michael Shi, Xudong Zhao, Guangdeng Zong, & Huiyan Zhang. (2024). Distributed Adaptive Sampled-Data Security Tracking Control for Uncertain Heterogeneous Multi-Agents Systems Under DoS Attacks. IEEE Transactions on Green Communications and Networking. 8(4). 1385–1397. 32 indexed citations
4.
Zhang, Qi, Jian Zhang, Haijun Zhong, et al.. (2023). Phase I study of MSB2311, a novel pH-dependent anti-PD-L1 monoclonal antibody, treating patients with advanced solid tumors and lymphoma. Cancer Immunology Immunotherapy. 72(8). 2729–2739. 1 indexed citations
5.
Xu, Jianming, Jianping Xiong, Shanzhi Gu, et al.. (2023). A phase 2 study of HMPL-453, a selective FGFR tyrosine kinase inhibitor (TKI), in patients with previously treated advanced cholangiocarcinoma containing FGFR2 fusions.. Journal of Clinical Oncology. 41(16_suppl). e16118–e16118. 1 indexed citations
6.
Kutlar, Abdullah, Julie Kanter, Darla Liles, et al.. (2018). Effect of crizanlizumab on pain crises in subgroups of patients with sickle cell disease: A SUSTAIN study analysis. American Journal of Hematology. 94(1). 55–61. 88 indexed citations
7.
Kanter, Julie, Abdullah Kutlar, Darla Liles, et al.. (2017). Crizanlizumab 5.0 Mg/Kg Increased the Time to First on-Treatment Sickle Cell Pain Crisis: A Subgroup Analysis of the Phase II Sustain Study. Blood. 130. 613–613. 1 indexed citations
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Escudier, Bernard, Viktor Grünwald, Alain Ravaud, et al.. (2014). Phase II Results of Dovitinib (TKI258) in Patients with Metastatic Renal Cell Cancer. Clinical Cancer Research. 20(11). 3012–3022. 49 indexed citations
10.
André, Fabrice, Thomas Bachelot, Mario Campone, et al.. (2013). Targeting FGFR with Dovitinib (TKI258): Preclinical and Clinical Data in Breast Cancer. Clinical Cancer Research. 19(13). 3693–3702. 251 indexed citations
11.
Angevin, Eric, José A. López-Martín, Chia‐Chi Lin, et al.. (2013). Phase I Study of Dovitinib (TKI258), an Oral FGFR, VEGFR, and PDGFR Inhibitor, in Advanced or Metastatic Renal Cell Carcinoma. Clinical Cancer Research. 19(5). 1257–1268. 103 indexed citations
12.
Shi, Michael, et al.. (2012). USCACA hosted symposiums at the 7th CACA meeting and the 15th CSCO meeting in Beijing. Chinese Journal of Cancer. 31(11). 505–6. 2 indexed citations
13.
Kim, Kevin B., Jason Chesney, Douglas Robinson, et al.. (2011). Phase I/II and Pharmacodynamic Study of Dovitinib (TKI258), an Inhibitor of Fibroblast Growth Factor Receptors and VEGF Receptors, in Patients with Advanced Melanoma. Clinical Cancer Research. 17(23). 7451–7461. 100 indexed citations
14.
James, Michael R., Richard B. Roth, Michael Shi, et al.. (2005). BRAF Polymorphisms and Risk of Melanocytic Neoplasia. Journal of Investigative Dermatology. 125(6). 1252–1258. 19 indexed citations
15.
Nelson, Matthew R., George E. Marnellos, Stefan Kammerer, et al.. (2004). Large-Scale Validation of Single Nucleotide Polymorphisms in Gene Regions. Genome Research. 14(8). 1664–1668. 80 indexed citations
16.
Kammerer, Stefan, Lora Hamuro, Yuliang Ma, et al.. (2003). Amino acid variant in the kinase binding domain of dual-specific A kinase-anchoring protein 2: A disease susceptibility polymorphism. Proceedings of the National Academy of Sciences. 100(7). 4066–4071. 80 indexed citations
17.
Guo, Renfeng, Alex B. Lentsch, Roscoe L. Warner, et al.. (2001). Regulatory Effects of Eotaxin on Acute Lung Inflammatory Injury. The Journal of Immunology. 166(8). 5208–5218. 20 indexed citations
18.
Chong, Inn‐Wen, Michael Shi, Jennifer A. Love, David C. Christiani, & Joseph Paulauskis. (2000). Regulation of Chemokine mRNA Expression in a Rat Model of Vanadium-Induced Pulmonary Inflammation. Inflammation. 24(6). 505–517. 18 indexed citations
19.
Shi, Michael, Inn‐Wen Chong, Nancy C. Long, et al.. (1998). Functional Characterization of Recombinant Rat Macrophage Inflammatory Protein-1α and mRNA Expression in Pulmonary Inflammation. Inflammation. 22(1). 29–43. 15 indexed citations
20.
Kugelman, Amir, Henry A. Choy, Run Liu, et al.. (1994). γ-Glutamyl Transpeptidase is Increased by Oxidative Stress in Rat Alveolar L2 Epithelial Cells. American Journal of Respiratory Cell and Molecular Biology. 11(5). 586–592. 142 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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