Jad Maamary

2.4k total citations
18 papers, 1.8k citations indexed

About

Jad Maamary is a scholar working on Immunology, Epidemiology and Molecular Biology. According to data from OpenAlex, Jad Maamary has authored 18 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Immunology, 9 papers in Epidemiology and 7 papers in Molecular Biology. Recurrent topics in Jad Maamary's work include Influenza Virus Research Studies (8 papers), Respiratory viral infections research (8 papers) and Monoclonal and Polyclonal Antibodies Research (7 papers). Jad Maamary is often cited by papers focused on Influenza Virus Research Studies (8 papers), Respiratory viral infections research (8 papers) and Monoclonal and Polyclonal Antibodies Research (7 papers). Jad Maamary collaborates with scholars based in United States, Germany and Czechia. Jad Maamary's co-authors include Jeffrey V. Ravetch, Andrew Pincetic, Taia T. Wang, Stylianos Bournazos, Rony Dahan, Peter Palese, Gene S. Tan, Florian Krammer, David J. DiLillo and Natalie Pica and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nature Immunology.

In The Last Decade

Jad Maamary

18 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jad Maamary United States 14 973 777 731 712 261 18 1.8k
Adam L. Corper United States 15 834 0.9× 576 0.7× 746 1.0× 398 0.6× 257 1.0× 24 1.8k
Erik Depla Belgium 23 485 0.5× 697 0.9× 482 0.7× 1.6k 2.2× 308 1.2× 48 2.7k
Bryan Briney United States 23 1.2k 1.2× 825 1.1× 935 1.3× 320 0.4× 420 1.6× 40 2.2k
Weizao Chen United States 26 734 0.8× 840 1.1× 1.0k 1.4× 184 0.3× 268 1.0× 59 2.1k
Marie‐France del Guercio United States 18 1.3k 1.4× 363 0.5× 904 1.2× 354 0.5× 174 0.7× 21 1.9k
Irene Graham United States 20 796 0.8× 175 0.2× 450 0.6× 995 1.4× 395 1.5× 28 1.9k
Ira Berkower United States 21 803 0.8× 534 0.7× 942 1.3× 356 0.5× 173 0.7× 45 1.9k
Jonathan Rothbard United Kingdom 11 1.2k 1.2× 341 0.4× 816 1.1× 292 0.4× 288 1.1× 13 2.1k
Andrew Worth United Kingdom 16 1.1k 1.1× 266 0.3× 333 0.5× 214 0.3× 110 0.4× 23 1.5k
Masayuki Kuraoka United States 20 1.2k 1.3× 260 0.3× 455 0.6× 480 0.7× 218 0.8× 44 1.8k

Countries citing papers authored by Jad Maamary

Since Specialization
Citations

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

Fields of papers citing papers by Jad Maamary

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jad Maamary

This figure shows the co-authorship network connecting the top 25 collaborators of Jad Maamary. A scholar is included among the top collaborators of Jad Maamary 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 Jad Maamary. Jad Maamary is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Jawa, Vibha, Jad Maamary, Michael D. Swanson, Shuli Zhang, & Diana Montgomery. (2022). Implementing a Clinical Immunogenicity Strategy using Preclinical Risk Assessment Outputs. Journal of Pharmaceutical Sciences. 111(4). 960–969. 9 indexed citations
2.
Příhoda, David, Jad Maamary, Andrew B. Waight, et al.. (2022). BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning. mAbs. 14(1). 2020203–2020203. 108 indexed citations
3.
Jawa, Vibha, et al.. (2021). Competition-Based Cell Assay Employing Soluble T Cell Receptors to Assess MHC Class II Antigen Processing and Presentation. The AAPS Journal. 23(2). 26–26. 4 indexed citations
4.
Westera, Liset, et al.. (2019). Poly-ADP Ribosyl Polymerase 1 (PARP1) Regulates Influenza A Virus Polymerase. Advances in Virology. 2019. 1–11. 12 indexed citations
5.
Bournazos, Stylianos, Taia T. Wang, Rony Dahan, Jad Maamary, & Jeffrey V. Ravetch. (2017). Signaling by Antibodies: Recent Progress. Annual Review of Immunology. 35(1). 285–311. 163 indexed citations
6.
Maamary, Jad, Taia T. Wang, Gene S. Tan, Peter Palese, & Jeffrey V. Ravetch. (2017). Increasing the breadth and potency of response to the seasonal influenza virus vaccine by immune complex immunization. Proceedings of the National Academy of Sciences. 114(38). 10172–10177. 41 indexed citations
7.
Maamary, Jad, Gene S. Tan, Stylianos Bournazos, et al.. (2015). Anti-HA Glycoforms Drive B Cell Affinity Selection and Determine Influenza Vaccine Efficacy. Cell. 162(1). 160–169. 154 indexed citations
8.
Maamary, Jad, et al.. (2015). Protection in antibody- and T cell-mediated autoimmune diseases by antiinflammatory IgG Fcs requires type II FcRs. Proceedings of the National Academy of Sciences. 112(18). E2385–94. 84 indexed citations
9.
Wang, Taia T., Jad Maamary, Sarah J. Schlesinger, & Jeffrey V. Ravetch. (2015). IgG anti-HA Fc glycoform modulation is predictive of influenza vaccine efficacy (IRC10P.412). The Journal of Immunology. 194(1_Supplement). 196.10–196.10. 1 indexed citations
10.
Pincetic, Andrew, Stylianos Bournazos, David J. DiLillo, et al.. (2014). Type I and type II Fc receptors regulate innate and adaptive immunity. Nature Immunology. 15(8). 707–716. 365 indexed citations
11.
Sondermann, Peter, Andrew Pincetic, Jad Maamary, Katja Lammens, & Jeffrey V. Ravetch. (2013). General mechanism for modulating immunoglobulin effector function. Proceedings of the National Academy of Sciences. 110(24). 9868–9872. 176 indexed citations
12.
Ortigoza, Mila B., Oliver Dibben, Jad Maamary, et al.. (2012). A Novel Small Molecule Inhibitor of Influenza A Viruses that Targets Polymerase Function and Indirectly Induces Interferon. PLoS Pathogens. 8(4). e1002668–e1002668. 37 indexed citations
13.
Maamary, Jad, Natalie Pica, Alan Belicha‐Villanueva, et al.. (2012). Attenuated Influenza Virus Construct with Enhanced Hemagglutinin Protein Expression. Journal of Virology. 86(10). 5782–5790. 16 indexed citations
14.
Pica, Natalie, Rong Hai, Florian Krammer, et al.. (2012). Hemagglutinin stalk antibodies elicited by the 2009 pandemic influenza virus as a mechanism for the extinction of seasonal H1N1 viruses. Proceedings of the National Academy of Sciences. 109(7). 2573–2578. 214 indexed citations
15.
Belicha‐Villanueva, Alan, Juan R. Rodríguez-Madoz, Jad Maamary, et al.. (2012). Recombinant Influenza A Viruses with Enhanced Levels of PB1 and PA Viral Protein Expression. Journal of Virology. 86(10). 5926–5930. 16 indexed citations
16.
Hai, Rong, Florian Krammer, Gene S. Tan, et al.. (2012). Influenza Viruses Expressing Chimeric Hemagglutinins: Globular Head and Stalk Domains Derived from Different Subtypes. Journal of Virology. 86(10). 5774–5781. 228 indexed citations
17.
Bortz, Eric, Liset Westera, Jad Maamary, et al.. (2011). Host- and Strain-Specific Regulation of Influenza Virus Polymerase Activity by Interacting Cellular Proteins. mBio. 2(4). 148 indexed citations
18.
Maamary, Jad, Qinshan Gao, Adolfo Garcı́a-Sastre, et al.. (2010). Newcastle Disease Virus Expressing a Dendritic Cell-Targeted HIV Gag Protein Induces a Potent Gag-Specific Immune Response in Mice. Journal of Virology. 85(5). 2235–2246. 30 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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