John J. Monaco

7.5k total citations
75 papers, 6.3k citations indexed

About

John J. Monaco is a scholar working on Immunology, Molecular Biology and Oncology. According to data from OpenAlex, John J. Monaco has authored 75 papers receiving a total of 6.3k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Immunology, 41 papers in Molecular Biology and 11 papers in Oncology. Recurrent topics in John J. Monaco's work include Ubiquitin and proteasome pathways (32 papers), Immune Cell Function and Interaction (32 papers) and Immunotherapy and Immune Responses (28 papers). John J. Monaco is often cited by papers focused on Ubiquitin and proteasome pathways (32 papers), Immune Cell Function and Interaction (32 papers) and Immunotherapy and Immune Responses (28 papers). John J. Monaco collaborates with scholars based in United States, United Kingdom and Mexico. John J. Monaco's co-authors include Michelle Attaya, Michael G. Brown, Sungae Cho, James J. Driscoll, Dipankar Nandi, Hugh O. McDevitt, Daniel Finley, Robert A. Colbert, Thomas A. Griffin and Miguel Cruz and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

John J. Monaco

72 papers receiving 6.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John J. Monaco United States 38 3.7k 3.3k 1.2k 814 656 75 6.3k
Christopher C. Norbury United States 29 3.3k 0.9× 2.5k 0.8× 757 0.6× 1.3k 1.6× 386 0.6× 64 6.4k
M P Beckmann United States 33 3.8k 1.0× 1.9k 0.6× 1.7k 1.4× 583 0.7× 457 0.7× 44 6.9k
Masaki Yasukawa Japan 47 4.0k 1.1× 2.0k 0.6× 2.6k 2.2× 1.3k 1.6× 821 1.3× 249 8.1k
Judith C. Gasson United States 42 3.4k 0.9× 2.6k 0.8× 1.2k 1.0× 406 0.5× 738 1.1× 78 7.4k
Mehrdad Matloubian United States 31 6.4k 1.7× 3.4k 1.0× 1.6k 1.3× 1.0k 1.2× 389 0.6× 53 9.8k
Hisashi Arase Japan 49 6.1k 1.7× 1.8k 0.5× 1.2k 1.0× 1.7k 2.1× 520 0.8× 153 8.8k
Russell D. Salter United States 36 4.3k 1.2× 2.2k 0.7× 760 0.6× 561 0.7× 391 0.6× 75 6.3k
Ted H. Hansen United States 53 9.1k 2.5× 2.6k 0.8× 1.5k 1.2× 2.0k 2.4× 585 0.9× 174 11.5k
Karsten Mahnke Germany 47 7.3k 2.0× 1.9k 0.6× 1.4k 1.2× 554 0.7× 398 0.6× 89 9.1k
Daisuke Kitamura Japan 47 6.4k 1.7× 3.2k 1.0× 1.3k 1.1× 675 0.8× 827 1.3× 148 9.8k

Countries citing papers authored by John J. Monaco

Since Specialization
Citations

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

Fields of papers citing papers by John J. Monaco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John J. Monaco. 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 John J. Monaco. The network helps show where John J. Monaco may publish in the future.

Co-authorship network of co-authors of John J. Monaco

This figure shows the co-authorship network connecting the top 25 collaborators of John J. Monaco. A scholar is included among the top collaborators of John J. Monaco 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 John J. Monaco. John J. Monaco 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.
Miller, William E., et al.. (2013). Pertussis Toxin B-Pentamer Mediates Intercellular Transfer of Membrane Proteins and Lipids. PLoS ONE. 8(9). e72885–e72885.
2.
Rockwell, Cheryl E., John J. Monaco, & Nilofer Qureshi. (2012). A Critical Role for the Inducible Proteasomal Subunits LMP7 and MECL1 in Cytokine Production by Activated Murine Splenocytes. Pharmacology. 89(3-4). 117–126. 14 indexed citations
3.
Pang, Ken C., et al.. (2006). Immunoproteasome Subunit Deficiencies Impact Differentially on Two Immunodominant Influenza Virus-Specific CD8+ T Cell Responses. The Journal of Immunology. 177(11). 7680–7688. 53 indexed citations
4.
Barton, Lance F., Herbert A. Runnels, Todd D. Schell, et al.. (2004). Immune Defects in 28-kDa Proteasome Activator γ-Deficient Mice. The Journal of Immunology. 172(6). 3948–3954. 89 indexed citations
5.
Barton, Lance F., Miguel Cruz, Reshma Rangwala, George S. Deepe, & John J. Monaco. (2002). Regulation of Immunoproteasome Subunit Expression In Vivo Following Pathogenic Fungal Infection. The Journal of Immunology. 169(6). 3046–3052. 67 indexed citations
6.
Wenderfer, Scott E., et al.. (2000). Identification of 40 Genes on a 1-Mb Contig around the IL-4 Cytokine Family Gene Cluster on Mouse Chromosome 11. Genomics. 63(3). 354–373. 9 indexed citations
7.
Elenich, Laura A., Dipankar Nandi, Alissa Kent, et al.. (1999). The complete primary structure of mouse 20S proteasomes. Immunogenetics. 49(10). 835–842. 36 indexed citations
8.
Hermel, Evan, et al.. (1999). Isolation and mapping of the rabbit DM genes. Immunogenetics. 49(4). 295–302. 7 indexed citations
9.
Oliva, Alessandra, Audrey Kinter, Mauro Vaccarezza, et al.. (1998). Natural killer cells from human immunodeficiency virus (HIV)-infected individuals are an important source of CC-chemokines and suppress HIV-1 entry and replication in vitro.. Journal of Clinical Investigation. 102(1). 223–231. 233 indexed citations
10.
Cruz, Miguel, Dipankar Nandi, Klavs B. Hendil, & John J. Monaco. (1997). Cloning and characterization of mouse Lmp3 cDNA, encoding a proteasome β subunit. Gene. 190(2). 251–256. 11 indexed citations
11.
Monaco, John J., et al.. (1996). Peptide transport in antigen presentation. Current Opinion in Hematology. 3(1). 19–26. 7 indexed citations
12.
Monaco, John J., et al.. (1995). Characterization and mapping of the gene encoding mouse proteasome subunit DELTA (Lmp19). Immunogenetics. 42(1). 28–34. 11 indexed citations
13.
Morris, Phillip, Jeffrey A. Shaman, Michelle Attaya, et al.. (1994). An essential role for HLA–DM in antigen presentation by class II major histocompatibility molecules. Nature. 368(6471). 551–554. 320 indexed citations
14.
Driscoll, James J., Michael G. Brown, Daniel Finley, & John J. Monaco. (1993). MHC-linked LMP gene products specifically alter peptidase activities of the proteasome. Nature. 365(6443). 262–264. 396 indexed citations
15.
Monaco, John J.. (1993). Structure and function of genes in the MHC class II region. Current Opinion in Immunology. 5(1). 17–20. 20 indexed citations
16.
Brown, Michael G. & John J. Monaco. (1993). Biochemical Purification of Distinct Proteasome Subsets. PubMed. 47(4-6). 343–353. 16 indexed citations
17.
Monaco, John J.. (1992). Genes in the MHC that may affect antigen processing. Current Opinion in Immunology. 4(1). 70–73. 21 indexed citations
18.
Endert, Peter Van, et al.. (1992). Genomic polymorphism, recombination, and linkage disequilibrium in human major histocompatibility complex-encoded antigen-processing genes.. Proceedings of the National Academy of Sciences. 89(23). 11594–11597. 81 indexed citations
19.
Brown, Michael G., James J. Driscoll, & John J. Monaco. (1991). Structural and serological similarity of MHC-linked LMP and proteasome (multicatalytic proteinase) complexes. Nature. 353(6342). 355–357. 267 indexed citations
20.
Boos, Stephen C., John J. Monaco, & Paul Gottlieb. (1978). Production of congenic Anti-Lyt-3.1 sera. Immunogenetics. 7(1). 165–168. 3 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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