Laura Bies

1.3k total citations · 1 hit paper
8 papers, 976 citations indexed

About

Laura Bies is a scholar working on Oncology, Immunology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Laura Bies has authored 8 papers receiving a total of 976 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Oncology, 7 papers in Immunology and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Laura Bies's work include CAR-T cell therapy research (7 papers), Immunotherapy and Immune Responses (6 papers) and Virus-based gene therapy research (2 papers). Laura Bies is often cited by papers focused on CAR-T cell therapy research (7 papers), Immunotherapy and Immune Responses (6 papers) and Virus-based gene therapy research (2 papers). Laura Bies collaborates with scholars based in Netherlands, United Kingdom and Germany. Laura Bies's co-authors include Ton N. Schumacher, Carsten Linnemann, John B.A.G. Haanen, Gavin Bendle, Els M.E. Verdegaal, Sam Behjati, Remko Schotte, Henk Hilkmann, Hergen Spits and Sjoerd H. van der Burg and has published in prestigious journals such as Nature Medicine, Journal of Clinical Oncology and The Journal of Immunology.

In The Last Decade

Laura Bies

6 papers receiving 966 citations

Hit Papers

High-throughput epitope discovery reveals frequent recogn... 2014 2026 2018 2022 2014 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laura Bies Netherlands 5 819 700 293 217 62 8 976
Matthias Leisegang Germany 20 838 1.0× 711 1.0× 283 1.0× 249 1.1× 91 1.5× 35 1.0k
Smita S. Chandran United States 13 670 0.8× 682 1.0× 261 0.9× 131 0.6× 57 0.9× 19 1.0k
Marie-Andrée Forget United States 11 748 0.9× 500 0.7× 263 0.9× 150 0.7× 30 0.5× 12 947
Srinivas S. Somanchi United States 15 880 1.1× 1.1k 1.6× 224 0.8× 102 0.5× 27 0.4× 30 1.3k
Xuexiang Du United States 10 586 0.7× 504 0.7× 204 0.7× 96 0.4× 51 0.8× 21 856
Clay Lyddane United States 7 626 0.8× 455 0.7× 307 1.0× 247 1.1× 47 0.8× 7 909
Chiara Casati Italy 13 563 0.7× 579 0.8× 274 0.9× 147 0.7× 26 0.4× 13 911
Nikolaos Zacharakis United States 5 596 0.7× 478 0.7× 257 0.9× 83 0.4× 75 1.2× 9 771
Dirk M. van der Steen Netherlands 14 440 0.5× 432 0.6× 283 1.0× 145 0.7× 49 0.8× 28 747
Kasia Trebska-McGowan United States 6 853 1.0× 760 1.1× 251 0.9× 91 0.4× 97 1.6× 9 1.0k

Countries citing papers authored by Laura Bies

Since Specialization
Citations

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

Fields of papers citing papers by Laura Bies

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laura Bies

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

All Works

8 of 8 papers shown
1.
Ma, Jennifer, Xiangjun Kong, Paula Kroon, et al.. (2024). Non-clinical evaluation of NT-175, an autologous T cell product engineered to express an HLA-A*02:01-restricted TCR targeting TP53 R175H and resistant to TGF-b inhibition.. Journal of Clinical Oncology. 42(16_suppl). 2560–2560.
2.
Linnemann, Carsten, Marit M. van Buuren, Laura Bies, et al.. (2016). High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4(+) T cells in human melanoma (vol 21, pg 81, 2015). Pure Amsterdam UMC. 22(10).
3.
Bunse, Mario, Gavin Bendle, Carsten Linnemann, et al.. (2014). RNAi-mediated TCR Knockdown Prevents Autoimmunity in Mice Caused by Mixed TCR Dimers Following TCR Gene Transfer. Molecular Therapy. 22(11). 1983–1991. 52 indexed citations
4.
Linnemann, Carsten, Marit M. van Buuren, Laura Bies, et al.. (2014). High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4+ T cells in human melanoma. Nature Medicine. 21(1). 81–85. 527 indexed citations breakdown →
5.
Oostvogels, Rimke, Kim Wals, Mireille Toebes, et al.. (2014). Altered Peptide Ligands Revisited: Vaccine Design through Chemically Modified HLA-A2–Restricted T Cell Epitopes. The Journal of Immunology. 193(10). 4803–4813. 35 indexed citations
6.
Bendle, Gavin, Carsten Linnemann, Laura Bies, Ji‐Ying Song, & Ton N. Schumacher. (2013). Blockade of TGF-β Signaling Greatly Enhances the Efficacy of TCR Gene Therapy of Cancer. The Journal of Immunology. 191(6). 3232–3239. 36 indexed citations
7.
Kelderman, Sander, Bianca Heemskerk, Mireille Toebes, et al.. (2013). Antigen-specific TIL therapy for melanoma: a flexible platform for personalized cancer immunotherapy. Journal for ImmunoTherapy of Cancer. 1(S1). 2 indexed citations
8.
Bendle, Gavin, Carsten Linnemann, Anna I. Hooijkaas, et al.. (2010). Lethal graft-versus-host disease in mouse models of T cell receptor gene therapy. Nature Medicine. 16(5). 565–570. 324 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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