Andreas Meyerhans

9.1k total citations · 1 hit paper
173 papers, 6.9k citations indexed

About

Andreas Meyerhans is a scholar working on Virology, Immunology and Molecular Biology. According to data from OpenAlex, Andreas Meyerhans has authored 173 papers receiving a total of 6.9k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Virology, 61 papers in Immunology and 51 papers in Molecular Biology. Recurrent topics in Andreas Meyerhans's work include HIV Research and Treatment (80 papers), Immune Cell Function and Interaction (41 papers) and T-cell and B-cell Immunology (32 papers). Andreas Meyerhans is often cited by papers focused on HIV Research and Treatment (80 papers), Immune Cell Function and Interaction (41 papers) and T-cell and B-cell Immunology (32 papers). Andreas Meyerhans collaborates with scholars based in Germany, Spain and Russia. Andreas Meyerhans's co-authors include Simon Wain–Hobson, Jean‐Pierre Vartanian, Gennady Bocharov, Birgitta Åsjö, Rémi Cheynier, Martina Sester, Javier P. Martínez, Urban Sester, Juana Díez and Reinhard Maier and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Andreas Meyerhans

170 papers receiving 6.7k citations

Hit Papers

Temporal fluctuations in ... 1989 2026 2001 2013 1989 100 200 300 400 500

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Andreas Meyerhans 2.9k 2.1k 2.0k 1.9k 1.8k 173 6.9k
Baek Kim 3.5k 1.2× 2.4k 1.1× 3.2k 1.6× 2.3k 1.2× 2.2k 1.2× 187 7.5k
Marc Girard 2.7k 0.9× 3.0k 1.4× 3.0k 1.5× 1.3k 0.7× 1.8k 1.0× 215 8.2k
Kalle Saksela 3.3k 1.2× 2.3k 1.1× 2.8k 1.4× 2.0k 1.1× 1.1k 0.6× 123 7.6k
Michael A. Skinner 1.8k 0.6× 2.1k 1.0× 2.6k 1.3× 1.6k 0.8× 2.5k 1.4× 159 8.6k
Gerald Schochetman 3.6k 1.3× 3.4k 1.6× 1.3k 0.6× 1.1k 0.6× 1.8k 1.0× 138 6.6k
Robert F. Garry 1.0k 0.4× 3.2k 1.5× 1.6k 0.8× 1.3k 0.7× 1.9k 1.0× 227 7.1k
Frank Maldarelli 6.8k 2.4× 5.1k 2.4× 1.7k 0.9× 2.1k 1.1× 1.6k 0.9× 141 8.8k
Gerhard Hunsmann 2.7k 0.9× 1.6k 0.8× 2.3k 1.1× 2.2k 1.2× 1.6k 0.9× 219 7.2k
Shigeru Morikawa 1.6k 0.6× 4.7k 2.2× 1.9k 1.0× 1.8k 1.0× 2.3k 1.3× 361 9.8k
G. Darby 2.8k 1.0× 2.7k 1.3× 1.3k 0.6× 862 0.5× 2.8k 1.6× 74 6.0k

Countries citing papers authored by Andreas Meyerhans

Since Specialization
Citations

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

Fields of papers citing papers by Andreas Meyerhans

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andreas Meyerhans

This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Meyerhans. A scholar is included among the top collaborators of Andreas Meyerhans 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 Andreas Meyerhans. Andreas Meyerhans 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
2.
Casella, Valentina, Paula Cebollada Rica, Jordi Argilaguet, et al.. (2024). Anti-PD-L1 Immunotherapy of Chronic Virus Infection Improves Virus Control without Augmenting Tissue Damage by Fibrosis. Viruses. 16(5). 799–799. 1 indexed citations
3.
Casella, Valentina, Anna Esteve‐Codina, Mireia Pedragosa, et al.. (2023). Differential kinetics of splenic CD169+ macrophage death is one underlying cause of virus infection fate regulation. Cell Death and Disease. 14(12). 838–838. 2 indexed citations
4.
Casella, Valentina, et al.. (2023). Mathematical Model Predicting the Kinetics of Intracellular LCMV Replication. Mathematics. 11(21). 4454–4454. 1 indexed citations
5.
Sazonov, Igor, et al.. (2023). Stochastic Modelling of HIV-1 Replication in a CD4 T Cell with an IFN Response. Viruses. 15(2). 296–296. 2 indexed citations
6.
Grossman, Zvi, Andreas Meyerhans, & Gennady Bocharov. (2023). An integrative systems biology view of host-pathogen interactions: The regulation of immunity and homeostasis is concomitant, flexible, and smart. Frontiers in Immunology. 13. 4 indexed citations
7.
Karsonova, Antonina, et al.. (2022). Predicting the Kinetic Coordination of Immune Response Dynamics in SARS-CoV-2 Infection: Implications for Disease Pathogenesis. Mathematics. 10(17). 3154–3154. 12 indexed citations
8.
Sazonov, Igor, et al.. (2022). Sensitivity of SARS-CoV-2 Life Cycle to IFN Effects and ACE2 Binding Unveiled with a Stochastic Model. Viruses. 14(2). 403–403. 5 indexed citations
9.
Sazonov, Igor, et al.. (2021). Markov Chain-Based Stochastic Modelling of HIV-1 Life Cycle in a CD4 T Cell. Mathematics. 9(17). 2025–2025. 10 indexed citations
10.
Sazonov, Igor, et al.. (2021). Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling. Viruses. 13(9). 1735–1735. 19 indexed citations
11.
Sazonov, Igor, et al.. (2020). Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production. Mathematics. 8(8). 1207–1207. 5 indexed citations
12.
Argilaguet, Jordi, Mireia Pedragosa, Anna Esteve‐Codina, et al.. (2019). Systems analysis reveals complex biological processes during virus infection fate decisions. Genome Research. 29(6). 907–919. 18 indexed citations
13.
González‐Cao, María, Javier Martínez‐Picado, Niki Karachaliou, Rafael Rosell, & Andreas Meyerhans. (2018). Cancer immunotherapy of patients with HIV infection. Clinical & Translational Oncology. 21(6). 713–720. 21 indexed citations
14.
Sadiq, S. Kashif, Gilles Mirambeau, & Andreas Meyerhans. (2018). Equilibrium Model of Drug-Modulated GagPol-Embedded HIV-1 Reverse Transcriptase Dimerization to Enhance Premature Protease Activation. AIDS Research and Human Retroviruses. 34(9). 804–807. 1 indexed citations
15.
Carreras‐Sureda, Amado, Fanny Rubio-Moscardó, Àlex Olvera, et al.. (2016). Lymphocyte Activation Dynamics Is Shaped by Hereditary Components at Chromosome Region 17q12-q21. PLoS ONE. 11(11). e0166414–e0166414. 4 indexed citations
16.
Pirón, María, Antoni Plasència, Ana Martı́nez, et al.. (2015). Low Seroprevalence of West Nile Virus in Blood Donors from Catalonia, Spain. Vector-Borne and Zoonotic Diseases. 15(12). 782–784. 10 indexed citations
17.
Suspène, Rodolphe, Marie-Ming Aynaud, Stefanie Koch, et al.. (2011). Genetic Editing of Herpes Simplex Virus 1 and Epstein-Barr Herpesvirus Genomes by Human APOBEC3 Cytidine Deaminases in Culture and In Vivo. Journal of Virology. 85(15). 7594–7602. 111 indexed citations
18.
Sester, Urban, Martina Sester, Hans Köhler, et al.. (2007). Maintenance of HIV-Specific Central and Effector Memory CD4 and CD8 T Cells Requires Antigen Persistence. AIDS Research and Human Retroviruses. 23(4). 549–553. 9 indexed citations
19.
Scheller, Nicoletta, Patricia Resa‐Infante, Susana de la Luna, et al.. (2007). Identification of PatL1, a human homolog to yeast P body component Pat1. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1773(12). 1786–1792. 51 indexed citations
20.
Günther, Stephan, Gunhild Sommer, Uwe Plikat, et al.. (1997). Naturally Occurring Hepatitis B Virus Genomes Bearing the Hallmarks of Retroviral G → A Hypermutation. Virology. 235(1). 104–108. 70 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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