Martin Mück-Häusl

506 total citations
11 papers, 310 citations indexed

About

Martin Mück-Häusl is a scholar working on Molecular Biology, Genetics and Oncology. According to data from OpenAlex, Martin Mück-Häusl has authored 11 papers receiving a total of 310 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Genetics and 3 papers in Oncology. Recurrent topics in Martin Mück-Häusl's work include Virus-based gene therapy research (5 papers), CRISPR and Genetic Engineering (3 papers) and CAR-T cell therapy research (3 papers). Martin Mück-Häusl is often cited by papers focused on Virus-based gene therapy research (5 papers), CRISPR and Genetic Engineering (3 papers) and CAR-T cell therapy research (3 papers). Martin Mück-Häusl collaborates with scholars based in Germany, China and Netherlands. Martin Mück-Häusl's co-authors include Anja Ehrhardt, Wenli Zhang, Yuval Rinkevich, Juliane Wannemacher, Haifeng Ye, Simon Christ, Theresa Asen, Manish Solanki, Ulrike Protzer and Zsolt Ruzsics and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Nature Immunology.

In The Last Decade

Martin Mück-Häusl

9 papers receiving 309 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Martin Mück-Häusl Germany 8 114 94 76 66 59 11 310
Isabel Mirones Spain 13 143 1.3× 50 0.5× 163 2.1× 33 0.5× 124 2.1× 22 470
Rekha Samuel India 10 238 2.1× 166 1.8× 45 0.6× 19 0.3× 40 0.7× 12 451
Anna P. Lam United States 5 371 3.3× 107 1.1× 64 0.8× 26 0.4× 40 0.7× 7 569
Andrew M. Overmiller United States 9 270 2.4× 32 0.3× 129 1.7× 38 0.6× 47 0.8× 15 554
Kambiz Kamyab Hesari Iran 10 73 0.6× 45 0.5× 31 0.4× 26 0.4× 38 0.6× 30 327
Thomas Imhof Germany 12 138 1.2× 71 0.8× 26 0.3× 19 0.3× 44 0.7× 27 396
Linda Schmidt United States 10 114 1.0× 68 0.7× 29 0.4× 25 0.4× 25 0.4× 22 403
Montserrat Reyes Chile 12 210 1.8× 31 0.3× 61 0.8× 26 0.4× 70 1.2× 34 436
Arzina Jaffer Canada 8 171 1.5× 18 0.2× 96 1.3× 77 1.2× 43 0.7× 12 399
Norman Rieger Germany 8 331 2.9× 73 0.8× 87 1.1× 34 0.5× 36 0.6× 10 470

Countries citing papers authored by Martin Mück-Häusl

Since Specialization
Citations

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

Fields of papers citing papers by Martin Mück-Häusl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin Mück-Häusl. 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 Martin Mück-Häusl. The network helps show where Martin Mück-Häusl may publish in the future.

Co-authorship network of co-authors of Martin Mück-Häusl

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

All Works

11 of 11 papers shown
1.
Kadri, Safwen, Martin Mück-Häusl, Wei Han, et al.. (2025). A mesothelial differentiation gateway drives fibrosis. Nature Communications. 16(1). 8295–8295.
2.
Kosinska, Anna D., Martin Mück-Häusl, Chunkyu Ko, et al.. (2025). MVA-HBVac—A novel vaccine vector that allows pan-genotypic targeting of hepatitis B virus by therapeutic vaccination. Molecular Therapy — Nucleic Acids. 36(3). 102641–102641.
3.
4.
Wannemacher, Juliane, Simon Christ, Tim Koopmans, et al.. (2022). Neutrophils direct preexisting matrix to initiate repair in damaged tissues. Nature Immunology. 23(4). 518–531. 73 indexed citations
5.
6.
Jiang, Dongsheng, Simon Christ, Donovan Correa‐Gallegos, et al.. (2020). Injury triggers fascia fibroblast collective cell migration to drive scar formation through N-cadherin. Nature Communications. 11(1). 5653–5653. 96 indexed citations
7.
Festag, Julia, Simon P. Fräßle, Theresa Asen, et al.. (2019). Evaluation of a Fully Human, Hepatitis B Virus-Specific Chimeric Antigen Receptor in an Immunocompetent Mouse Model. Molecular Therapy. 27(5). 947–959. 41 indexed citations
8.
Mück-Häusl, Martin, Manish Solanki, Wenli Zhang, Zsolt Ruzsics, & Anja Ehrhardt. (2015). Ad 2.0: a novel recombineering platform for high-throughput generation of tailored adenoviruses. Nucleic Acids Research. 43(8). e50–e50. 28 indexed citations
9.
Behr, Michaël, Johanna K. Kaufmann, Patrick Ketzer, et al.. (2014). Adenoviruses Using the Cancer Marker EphA2 as a Receptor In Vitro and In Vivo by Genetic Ligand Insertion into Different Capsid Scaffolds. PLoS ONE. 9(4). e95723–e95723. 12 indexed citations
10.
Zhang, Wenli, Martin Mück-Häusl, Jichang Wang, et al.. (2013). Integration Profile and Safety of an Adenovirus Hybrid-Vector Utilizing Hyperactive Sleeping Beauty Transposase for Somatic Integration. PLoS ONE. 8(10). e75344–e75344. 30 indexed citations
11.
Haase, Rudolf, Martin Mück-Häusl, Wenli Zhang, et al.. (2013). A Novel Adenoviral Hybrid-vector System Carrying a Plasmid Replicon for Safe and Efficient Cell and Gene Therapeutic Applications. Molecular Therapy — Nucleic Acids. 2. e83–e83. 18 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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