Michael Farzan

46.4k total citations · 16 hit papers
178 papers, 30.4k citations indexed

About

Michael Farzan is a scholar working on Infectious Diseases, Immunology and Virology. According to data from OpenAlex, Michael Farzan has authored 178 papers receiving a total of 30.4k indexed citations (citations by other indexed papers that have themselves been cited), including 89 papers in Infectious Diseases, 68 papers in Immunology and 65 papers in Virology. Recurrent topics in Michael Farzan's work include HIV Research and Treatment (65 papers), SARS-CoV-2 and COVID-19 Research (47 papers) and Immune Cell Function and Interaction (38 papers). Michael Farzan is often cited by papers focused on HIV Research and Treatment (65 papers), SARS-CoV-2 and COVID-19 Research (47 papers) and Immune Cell Function and Interaction (38 papers). Michael Farzan collaborates with scholars based in United States, China and Germany. Michael Farzan's co-authors include Hyeryun Choe, Wenhui Li, Joseph Sodroski, Natalya Vasilieva, Michael J. Moore, Cody B. Jackson, I‐Chueh Huang, Swee Kee Wong, Jianhua Sui and Bing Chen and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Michael Farzan

176 papers receiving 29.9k citations

Hit Papers

Angiotensin-converting enzyme 2 is a functional receptor ... 1996 2026 2006 2016 2003 1996 2021 1996 2005 1000 2.0k 3.0k 4.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus
    2003 4.3k citations Wenhui Li, Michael J. Moore et al. profile →
  2. The β-Chemokine Receptors CCR3 and CCR5 Facilitate Infection by Primary HIV-1 Isolates
    1996 2.0k citations Hyeryun Choe, Michael Farzan et al. profile →
  3. Mechanisms of SARS-CoV-2 entry into cells
    2021 1.9k citations Cody B. Jackson, Michael Farzan et al. profile →
  4. The lymphocyte chemoattractant SDF-1 is a ligand for LESTR/fusin and blocks HIV-1 entry
    1996 1.7k citations Michael Farzan, Hyeryun Choe et al. profile →
  5. Structure of SARS Coronavirus Spike Receptor-Binding Domain Complexed with Receptor
    2005 1.5k citations Wenhui Li, Michael Farzan et al. profile →
  6. The IFITM Proteins Mediate Cellular Resistance to Influenza A H1N1 Virus, West Nile Virus, and Dengue Virus
    2009 1.0k citations I‐Chueh Huang, Michael Farzan et al. profile →
  7. CCR3 and CCR5 are co-receptors for HIV-1 infection of microglia
    1997 778 citations Michael Farzan, Hyeryun Choe et al. profile →
  8. Receptor and viral determinants of SARS‐coronavirus adaptation to human ACE2
    2005 735 citations Wenhui Li, Jianhua Sui et al. profile →
  9. Influenza A Virus NS1 Targets the Ubiquitin Ligase TRIM25 to Evade Recognition by the Host Viral RNA Sensor RIG-I
    2009 718 citations Michaela U. Gack, Randy A. Albrecht et al. Cell Host & Microbe profile →
  10. SARS-CoV-2 spike-protein D614G mutation increases virion spike density and infectivity
    2020 662 citations Lizhou Zhang, Cody B. Jackson et al. Nature Communications profile →
  11. The broad-spectrum antiviral functions of IFIT and IFITM proteins
    2012 646 citations Michael Diamond, Michael Farzan profile →
  12. ACE2 Receptor Expression and Severe Acute Respiratory Syndrome Coronavirus Infection Depend on Differentiation of Human Airway Epithelia
    2005 643 citations Michael Farzan et al. Journal of Virology profile →
  13. Tyrosine Sulfation of the Amino Terminus of CCR5 Facilitates HIV-1 Entry
    1999 577 citations Michael Farzan, Tajib A. Mirzabekov et al. profile →
  14. A 193-Amino Acid Fragment of the SARS Coronavirus S Protein Efficiently Binds Angiotensin-converting Enzyme 2
    2004 547 citations Swee Kee Wong, Wenhui Li et al. Journal of Biological Chemistry profile →
  15. Distinct Patterns of IFITM-Mediated Restriction of Filoviruses, SARS Coronavirus, and Influenza A Virus
    2011 471 citations I‐Chueh Huang, Charles C. Bailey et al. PLoS Pathogens profile →
  16. Effect of mRNA-LNP components of two globally-marketed COVID-19 vaccines on efficacy and stability
    2023 105 citations Lizhou Zhang, Amrita Ojha et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Farzan United States 76 15.8k 9.9k 7.2k 6.5k 4.4k 178 30.4k
Hyeryun Choe United States 56 11.7k 0.7× 7.5k 0.7× 5.0k 0.7× 6.5k 1.0× 2.5k 0.6× 97 22.8k
Stefan Pöhlmann Germany 64 21.3k 1.4× 5.5k 0.6× 5.8k 0.8× 2.4k 0.4× 3.9k 0.9× 231 30.8k
Michael B. A. Oldstone United States 107 9.7k 0.6× 19.0k 1.9× 7.7k 1.1× 4.8k 0.7× 10.7k 2.4× 536 39.7k
Barney S. Graham United States 87 15.6k 1.0× 7.2k 0.7× 5.9k 0.8× 3.8k 0.6× 13.9k 3.2× 359 30.9k
Shane Crotty United States 80 8.0k 0.5× 17.1k 1.7× 6.1k 0.8× 3.5k 0.5× 4.3k 1.0× 190 28.9k
Linqi Zhang China 53 10.9k 0.7× 4.9k 0.5× 3.5k 0.5× 7.1k 1.1× 3.1k 0.7× 229 18.2k
Gary J. Nabel United States 108 10.9k 0.7× 14.3k 1.4× 14.1k 2.0× 9.8k 1.5× 8.8k 2.0× 365 38.2k
Michael G. Katze United States 91 6.8k 0.4× 10.2k 1.0× 10.9k 1.5× 2.1k 0.3× 9.7k 2.2× 300 27.8k
Dimiter S. Dimitrov United States 74 5.6k 0.4× 6.4k 0.6× 7.1k 1.0× 6.9k 1.1× 3.2k 0.7× 364 20.7k
Barton F. Haynes United States 90 8.5k 0.5× 16.7k 1.7× 8.5k 1.2× 14.9k 2.3× 5.7k 1.3× 432 34.7k

Countries citing papers authored by Michael Farzan

Since Specialization
Citations

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

Fields of papers citing papers by Michael Farzan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Farzan

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Farzan. A scholar is included among the top collaborators of Michael Farzan 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 Farzan. Michael Farzan 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.
Zaccaria, Marco, Luigi Genovese, William Harbutt Dawson, et al.. (2024). Predicting potential SARS-CoV-2 mutations of concern via full quantum mechanical modelling. Journal of The Royal Society Interface. 21(211). 20230614–20230614. 1 indexed citations
2.
Hopkins, Loren, Sebastian Fuchs, José M. Martinez-Navío, et al.. (2024). In vivo evolution of env in SHIV-AD8EO-infected rhesus macaques after AAV-vectored delivery of eCD4-Ig. Molecular Therapy. 33(2). 560–579.
3.
Smith, Emery, Meredith E. Davis-Gardner, Rubén D. Garcia-Ordoñez, et al.. (2023). High throughput screening for drugs that inhibit 3C-like protease in SARS-CoV-2. SLAS DISCOVERY. 28(3). 95–101. 13 indexed citations
4.
Zaccaria, Marco, Luigi Genovese, William Harbutt Dawson, et al.. (2022). Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures. PNAS Nexus. 1(5). pgac180–pgac180. 10 indexed citations
5.
Evans, David T., et al.. (2021). Predicting the efficacy of COVID-19 convalescent plasma donor units with the Lumit Dx anti-receptor binding domain assay. PLoS ONE. 16(7). e0253551–e0253551. 4 indexed citations
6.
Mou, Huihui, Brian D. Quinlan, Haiyong Peng, et al.. (2021). Mutations derived from horseshoe bat ACE2 orthologs enhance ACE2-Fc neutralization of SARS-CoV-2. PLoS Pathogens. 17(4). e1009501–e1009501. 34 indexed citations
7.
Tran, Mai H., HaJeung Park, Christopher L. Nobles, et al.. (2021). A more efficient CRISPR-Cas12a variant derived from Lachnospiraceae bacterium MA2020. Molecular Therapy — Nucleic Acids. 24. 40–53. 29 indexed citations
8.
Ou, Tianling, Wenhui He, Brian D. Quinlan, et al.. (2021). Reprogramming of the heavy-chain CDR3 regions of a human antibody repertoire. Molecular Therapy. 30(1). 184–197. 10 indexed citations
9.
Zhang, Lizhou, Cody B. Jackson, Huihui Mou, et al.. (2020). SARS-CoV-2 spike-protein D614G mutation increases virion spike density and infectivity. Nature Communications. 11(1). 6013–6013. 662 indexed citations breakdown →
10.
Gorman, Matthew J., Subhajit Poddar, Michael Farzan, & Michael Diamond. (2016). The Interferon-Stimulated Gene Ifitm3 Restricts West Nile Virus Infection and Pathogenesis. Journal of Virology. 90(18). 8212–8225. 72 indexed citations
11.
Jemielity, Stephanie, Ying Chan, Asim A. Ahmed, et al.. (2013). TIM-family Proteins Promote Infection of Multiple Enveloped Viruses through Virion-associated Phosphatidylserine. PLoS Pathogens. 9(3). e1003232–e1003232. 272 indexed citations
12.
Suzuki, Tatsuo, Jingping Zhang, Shoko Miyazawa, et al.. (2011). Association of membrane rafts and postsynaptic density: proteomics, biochemical, and ultrastructural analyses. Journal of Neurochemistry. 119(1). 64–77. 58 indexed citations
13.
Gack, Michaela U., Randy A. Albrecht, Tomohiko Urano, et al.. (2009). Influenza A Virus NS1 Targets the Ubiquitin Ligase TRIM25 to Evade Recognition by the Host Viral RNA Sensor RIG-I. Cell Host & Microbe. 5(5). 439–449. 718 indexed citations breakdown →
14.
Liu, Chang C., Meng‐Lin Tsao, Jeremy H. Mills, et al.. (2008). Protein evolution with an expanded genetic code. Proceedings of the National Academy of Sciences. 105(46). 17688–17693. 116 indexed citations
15.
Hwang, W.C., Eugenio Santelli, Lukasz Jaroszewski, et al.. (2007). Structural Basis of Neutralization By a Human Anti-Sars Spike Protein Antibody, 80r. Journal of Biological Chemistry. 281. 2 indexed citations
16.
Petit, Chad M., Vladimir N. Chouljenko, Arun V. Iyer, et al.. (2006). Palmitoylation of the cysteine-rich endodomain of the SARS–coronavirus spike glycoprotein is important for spike-mediated cell fusion. Virology. 360(2). 264–274. 101 indexed citations
17.
Huang, Chih-chin, Miro Venturi, Shahzad Majeed, et al.. (2004). Structural basis of tyrosine sulfation and V H -gene usage in antibodies that recognize the HIV type 1 coreceptor-binding site on gp120. Proceedings of the National Academy of Sciences. 101(9). 2706–2711. 213 indexed citations
18.
Sui, Jianhua, Wenhui Li, Akikazu Murakami, et al.. (2004). Potent neutralization of severe acute respiratory syndrome (SARS) coronavirus by a human mAb to S1 protein that blocks receptor association. Proceedings of the National Academy of Sciences. 101(8). 2536–2541. 435 indexed citations
19.
Wang, Jianbin, Gregory J. Babcock, Hyeryun Choe, et al.. (2004). N-linked glycosylation in the CXCR4 N-terminus inhibits binding to HIV-1 envelope glycoproteins. Virology. 324(1). 140–150. 36 indexed citations
20.
Wojtowicz, Woj M., Michael Farzan, John L. Joyal, et al.. (2002). Stimulation of Enveloped Virus Infection by β-Amyloid Fibrils. Journal of Biological Chemistry. 277(38). 35019–35024. 59 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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