Márton Münz

555 total citations
10 papers, 323 citations indexed

About

Márton Münz is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Márton Münz has authored 10 papers receiving a total of 323 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 3 papers in Immunology and 3 papers in Cancer Research. Recurrent topics in Márton Münz's work include Genomics and Rare Diseases (2 papers), Protein Structure and Dynamics (2 papers) and Cancer Genomics and Diagnostics (2 papers). Márton Münz is often cited by papers focused on Genomics and Rare Diseases (2 papers), Protein Structure and Dynamics (2 papers) and Cancer Genomics and Diagnostics (2 papers). Márton Münz collaborates with scholars based in United Kingdom, Switzerland and United States. Márton Münz's co-authors include Philip C. Biggin, Jotun Hein, Gerton Lunter, Jacob D. Galson, Vincenzo Cerundolo, Anna Fowler, Dominic F. Kelly, Andrew J. Pollard, Johannes Trück and Elizabeth Clutterbuck and has published in prestigious journals such as Cancer Research, Scientific Reports and Frontiers in Immunology.

In The Last Decade

Márton Münz

10 papers receiving 323 citations

Peers

Márton Münz

MB

IMMUN

RNMI

EPIDE

GENET

Michelle M. Gomes United States

Cecilia Carnrot Sweden

Melanie L. Dart United States

Lorenz Chatwell Germany

Tomislav Čaval Netherlands

Jacek Mozdzanowski United States

Géza Ambrus Hungary

Philip Matharu United Kingdom

Henry G. Hocking Netherlands

Soumya G. Remesh United States

Michelle M. Gomes United States

Márton Münz

240 ×1.5

MB

134 ×0.9

IMMUN

83 ×1.0

RNMI

40 ×1.3

EPIDE

20 ×0.7

GENET

Citations per year, relative to Márton Münz Márton Münz (= 1×) peers Michelle M. Gomes

Countries citing papers authored by Márton Münz

Since Specialization

Citations

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

Fields of papers citing papers by Márton Münz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Márton Münz

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

All Works

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10 of 10 papers shown

1.

Mertz, Jennifer A., Stefan Kirov, Kaylyn E. Williamson, et al.. (2023). Abstract 2802: Development of a surrogate tissue pharmacodynamic (PD) assay for clinical use with FX-909, a novel inhibitor of the urothelial luminal lineage transcription factor peroxisome proliferator-activated receptor gamma (PPARG). Cancer Research. 83(7_Supplement). 2802–2802. 4 indexed citations

2.

Ruark, Elise, Márton Münz, Matthew Clarke, et al.. (2016). OpEx - a validated, automated pipeline optimised for clinical exome sequence analysis. Scientific Reports. 6(1). 31029–31029. 4 indexed citations

3.

Galson, Jacob D., Johannes Trück, Anna Fowler, et al.. (2015). Analysis of B Cell Repertoire Dynamics Following Hepatitis B Vaccination in Humans, and Enrichment of Vaccine-specific Antibody Sequences. EBioMedicine. 2(12). 2070–2079. 68 indexed citations

4.

Galson, Jacob D., Johannes Trück, Anna Fowler, et al.. (2015). In-Depth Assessment of Within-Individual and Inter-Individual Variation in the B Cell Receptor Repertoire. Frontiers in Immunology. 6. 531–531. 58 indexed citations

5.

Münz, Márton, Elise Ruark, Anthony Renwick, et al.. (2015). CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinical setting. Genome Medicine. 7(1). 76–76. 29 indexed citations

6.

Galson, Jacob D., Elizabeth Clutterbuck, Johannes Trück, et al.. (2015). BCR repertoire sequencing: different patterns of B‐cell activation after two Meningococcal vaccines. Immunology and Cell Biology. 93(10). 885–895. 57 indexed citations

7.

Ruark, Elise, Márton Münz, Anthony Renwick, et al.. (2015). The ICR1000 UK exome series: a resource of gene variation in an outbred population [version 1; referees: 2 approved]. 4. 2 indexed citations

8.

Münz, Márton, Jotun Hein, & Philip C. Biggin. (2012). The Role of Flexibility and Conformational Selection in the Binding Promiscuity of PDZ Domains. PLoS Computational Biology. 8(11). e1002749–e1002749. 61 indexed citations

9.

Münz, Márton & Philip C. Biggin. (2011). JGromacs: A Java Package for Analyzing Protein Simulations. Journal of Chemical Information and Modeling. 52(1). 255–259. 19 indexed citations

10.

Münz, Márton, Rune B. Lyngsø, Jotun Hein, & Philip C. Biggin. (2010). Dynamics based alignment of proteins: an alternative approach to quantify dynamic similarity. BMC Bioinformatics. 11(1). 188–188. 21 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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