Ralf Huss

5.2k total citations
111 papers, 3.9k citations indexed

About

Ralf Huss is a scholar working on Genetics, Oncology and Molecular Biology. According to data from OpenAlex, Ralf Huss has authored 111 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Genetics, 33 papers in Oncology and 28 papers in Molecular Biology. Recurrent topics in Ralf Huss's work include Mesenchymal stem cell research (35 papers), Hematopoietic Stem Cell Transplantation (23 papers) and Virus-based gene therapy research (18 papers). Ralf Huss is often cited by papers focused on Mesenchymal stem cell research (35 papers), Hematopoietic Stem Cell Transplantation (23 papers) and Virus-based gene therapy research (18 papers). Ralf Huss collaborates with scholars based in Germany, United States and South Africa. Ralf Huss's co-authors include Peter J. Nelson, Christiane J. Bruns, Hanno Nieß, H. Joachim Deeg, Claudius Conrad, Karl-Walter Jauch, Claudius Conrad, Karin Thalmeier, Christian Seliger and Mike Notohamiprodjo and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Circulation.

In The Last Decade

Ralf Huss

109 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ralf Huss Germany 35 1.4k 1.1k 971 907 696 111 3.9k
Reinhard Henschler Germany 38 1.7k 1.2× 1.7k 1.6× 870 0.9× 852 0.9× 758 1.1× 138 4.7k
Sonia A. Perez Greece 33 1.4k 1.0× 1.3k 1.1× 1.6k 1.6× 796 0.9× 1.9k 2.7× 95 4.2k
Erika L. Spaeth United States 18 1.8k 1.3× 1.6k 1.4× 1.9k 2.0× 549 0.6× 602 0.9× 33 4.0k
Charles K. F. Chan United States 26 690 0.5× 1.5k 1.4× 1.1k 1.1× 419 0.5× 1.4k 1.9× 59 4.2k
Vera S. Donnenberg United States 29 1.2k 0.8× 1.2k 1.1× 1.6k 1.6× 903 1.0× 695 1.0× 93 3.7k
Sowmya Viswanathan Canada 25 1.6k 1.1× 1.1k 0.9× 505 0.5× 858 0.9× 425 0.6× 97 3.3k
U. Krause Germany 27 1.6k 1.1× 1.0k 0.9× 565 0.6× 1.1k 1.2× 142 0.2× 96 3.4k
Stefan Scheding Sweden 29 1.1k 0.8× 786 0.7× 666 0.7× 365 0.4× 334 0.5× 91 2.7k
Joel A. Spencer United States 23 1.0k 0.7× 1.9k 1.7× 1.9k 1.9× 483 0.5× 720 1.0× 42 5.4k
Robert A.J. Oostendorp Germany 35 876 0.6× 1.6k 1.5× 664 0.7× 330 0.4× 1.1k 1.5× 109 3.8k

Countries citing papers authored by Ralf Huss

Since Specialization
Citations

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

Fields of papers citing papers by Ralf Huss

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ralf Huss

This figure shows the co-authorship network connecting the top 25 collaborators of Ralf Huss. A scholar is included among the top collaborators of Ralf Huss 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 Ralf Huss. Ralf Huss 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.
Huss, Ralf, Johannes Raffler, & Bruno Märkl. (2023). Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology. Cancer Reports. 6(7). e1796–e1796. 7 indexed citations
2.
Schiele, Stefan, Gerhard Schenkirsch, Matthias Anthuber, et al.. (2021). Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images. Cancers. 13(9). 2074–2074. 13 indexed citations
3.
Märkl, Bruno, et al.. (2020). Number of pathologists in Germany: comparison with European countries, USA, and Canada. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 478(2). 335–341. 61 indexed citations
4.
Schraml, Peter, Maria Athelogou, Thomas Hermanns, Ralf Huss, & Holger Moch. (2019). Specific immune cell and lymphatic vessel signatures identified by image analysis in renal cancer. Modern Pathology. 32(7). 1042–1052. 12 indexed citations
5.
Nößner, Elfriede, et al.. (2018). Fluorescent Polyvinylphosphonate Bioconjugates for Selective Cellular Delivery. Chemistry - A European Journal. 24(11). 2584–2587. 13 indexed citations
6.
Nelson, Peter J., et al.. (2018). Synthesis of next generation dual-responsive cross-linked nanoparticles and their application to anti-cancer drug delivery. Nanoscale. 10(34). 16062–16068. 15 indexed citations
7.
Nelson, Peter J., et al.. (2017). Precise synthesis of thermoresponsive polyvinylphosphonate-biomolecule conjugatesviathiol–ene click chemistry. Polymer Chemistry. 9(3). 284–290. 23 indexed citations
8.
Nieß, Hanno, Jobst C. von Einem, Michael Thomas, et al.. (2015). Treatment of advanced gastrointestinal tumors with genetically modified autologous mesenchymal stromal cells (TREAT-ME1): study protocol of a phase I/II clinical trial. BMC Cancer. 15(1). 237–237. 86 indexed citations
9.
Wegmeyer, Heike, Ann-Marie E Bröske, Mathias Leddin, et al.. (2013). Mesenchymal Stromal Cell Characteristics Vary Depending on Their Origin. Stem Cells and Development. 22(19). 2606–2618. 176 indexed citations
10.
Bao, Qi, Yue Zhao, Hanno Nieß, et al.. (2012). Mesenchymal Stem Cell-Based Tumor-Targeted Gene Therapy in Gastrointestinal Cancer. Stem Cells and Development. 21(13). 2355–2363. 43 indexed citations
11.
12.
Egaña, José Tomás, Fernando A. Fierro, Stefan Krüger, et al.. (2008). Use of Human Mesenchymal Cells to Improve Vascularization in a Mouse Model for Scaffold-Based Dermal Regeneration. Tissue Engineering Part A. 15(5). 1191–1200. 62 indexed citations
13.
Huber, Stephan, Christiane J. Bruns, G. Schmid, et al.. (2007). Inhibition of the mammalian target of rapamycin impedes lymphangiogenesis. Kidney International. 71(8). 771–777. 166 indexed citations
14.
Lüttichau, Irene von, Mike Notohamiprodjo, Christina Peters, et al.. (2005). Human Adult CD34 − Progenitor Cells Functionally Express the Chemokine Receptors CCR1, CCR4, CCR7, CXCR5, and CCR10 but Not CXCR4. Stem Cells and Development. 14(3). 329–336. 163 indexed citations
15.
Huss, Ralf. (2000). Perspectives on the Morphology and Biology of CD34-Negative Stem Cells. Journal of Hematotherapy & Stem Cell Research. 9(6). 783–793. 50 indexed citations
16.
Lange, Claudia, et al.. (1999). Hematopoietic Reconstitution of Syngeneic Mice with a Peripheral Blood-Derived, Monoclonal CD34-, Sca-1+, Thy-1low, c-kit+ Stem Cell Line. Journal of Hematotherapy & Stem Cell Research. 8(4). 335–342. 40 indexed citations
17.
Deeg, H. Joachim, Kristy Seidel, David S. Hong, et al.. (1997). In vivo radioprotective effect of AcSDKP on canine myelopoiesis. Annals of Hematology. 74(3). 117–122. 10 indexed citations
18.
Huss, Ralf & H. Joachim Deeg. (1997). Intrathymic maturation of CD4+ T‐lymphocytes in an MHC class II deficient transplant model. Tissue Antigens. 49(1). 70–73. 1 indexed citations
19.
Huss, Ralf, et al.. (1995). Cyclosporine-induced apoptosis in CD4+ T lymphocytes and computer-simulated analysis: modeling a treatment scenario for HIV infection. Research in Immunology. 146(2). 101–108. 13 indexed citations
20.
Huss, Ralf & H. Joachim Deeg. (1994). MHC antigens and haemopoiesis. Transplant Immunology. 2(3). 171–175. 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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