Frédéric Lehmann

5.9k total citations · 1 hit paper
107 papers, 3.9k citations indexed

About

Frédéric Lehmann is a scholar working on Immunology, Oncology and Electrical and Electronic Engineering. According to data from OpenAlex, Frédéric Lehmann has authored 107 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Immunology, 41 papers in Oncology and 34 papers in Electrical and Electronic Engineering. Recurrent topics in Frédéric Lehmann's work include Immunotherapy and Immune Responses (34 papers), CAR-T cell therapy research (28 papers) and Immune Cell Function and Interaction (20 papers). Frédéric Lehmann is often cited by papers focused on Immunotherapy and Immune Responses (34 papers), CAR-T cell therapy research (28 papers) and Immune Cell Function and Interaction (20 papers). Frédéric Lehmann collaborates with scholars based in France, Belgium and United States. Frédéric Lehmann's co-authors include Thierry Boon, Pierre G. Coulie, Bernard Lethé, Vincent Brichard, Hideyuki Ikeda, Nicolas van Baren, Charles De Smet, Jean‐François Baurain, Alessandro Moretta and Hérvè Chambost and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and Blood.

In The Last Decade

Frédéric Lehmann

103 papers receiving 3.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Characterization of an Antigen That Is Recognized on a Melanoma Showing Partial HLA Loss by CTL Expressing an NK Inhibitory Receptor

1997 639 citations Hideyuki Ikeda, Bernard Lethé et al. Immunity profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Frédéric Lehmann France	30	2.6k	2.0k	1.3k	275	265	107	3.9k
Massimo D’Apuzzo United States	23	1.2k 0.5×	1.8k 0.9×	743 0.6×	188 0.7×	315 1.2×	73	3.3k
Dongguang Li China	21	1.1k 0.4×	1.1k 0.6×	498 0.4×	119 0.4×	84 0.3×	96	2.8k
Christian S. Hinrichs United States	34	5.6k 2.1×	6.1k 3.1×	1.8k 1.4×	316 1.1×	237 0.9×	88	8.7k
Jacob A. Klapper United States	20	1.4k 0.5×	2.1k 1.1×	504 0.4×	711 2.6×	103 0.4×	104	3.3k
Xianwen Ren China	29	2.7k 1.0×	2.5k 1.3×	2.7k 2.0×	623 2.3×	87 0.3×	79	6.3k
Lu Si China	33	964 0.4×	2.7k 1.3×	2.0k 1.5×	487 1.8×	94 0.4×	302	4.5k
Andreas G. Niethammer United States	27	980 0.4×	947 0.5×	1.1k 0.8×	725 2.6×	50 0.2×	46	2.6k
Yves Bertrand France	24	578 0.2×	464 0.2×	681 0.5×	115 0.4×	598 2.3×	153	2.7k
Kara L. Davis United States	21	1.4k 0.6×	1.0k 0.5×	2.6k 1.9×	200 0.7×	47 0.2×	74	4.4k
Pavel Sumazin United States	29	454 0.2×	658 0.3×	2.8k 2.1×	411 1.5×	73 0.3×	68	4.1k

Countries citing papers authored by Frédéric Lehmann

Since Specialization

Citations

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

Fields of papers citing papers by Frédéric Lehmann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Frédéric Lehmann

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

All Works

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20 of 20 papers shown

Lehmann, Frédéric & Wojciech Pieczynski. (2020). State estimation in pairwise Markov models with improved robustness using unbiased FIR filtering. Signal Processing. 172. 107568–107568. 3 indexed citations

Curigliano, Giuseppe, Vincenzo Bagnardi, Jamila Louahed, et al.. (2019). Expression of tumor-associated antigens in breast cancer subtypes. The Breast. 49. 202–209. 26 indexed citations

Lonez, Caroline, Eric Van Cutsem, Jean‐Pascal Machiels, et al.. (2019). Chimeric Antigen Receptor-T Cells for Targeting Solid Tumors: Current Challenges and Existing Strategies. BioDrugs. 33(5). 515–537. 47 indexed citations

Shmaliy, Yuriy S., Frédéric Lehmann, Shunyi Zhao, & Choon Ki Ahn. (2018). Comparing Robustness of the Kalman, <inline-formula> <tex-math notation="LaTeX">$H_\infty$</tex-math> </inline-formula>, and UFIR Filters. IEEE Transactions on Signal Processing. 66(13). 3447–3458. 84 indexed citations

Murad, Joana, Susanne H.C. Baumeister, Lillian Werner, et al.. (2018). Manufacturing development and clinical production of NKG2D chimeric antigen receptor–expressing T cells for autologous adoptive cell therapy. Cytotherapy. 20(7). 952–963. 46 indexed citations

Lonez, Caroline, Bikash Verma, Alain Hendlisz, et al.. (2017). Study protocol for THINK: a multinational open-label phase I study to assess the safety and clinical activity of multiple administrations of NKR-2 in patients with different metastatic tumour types. BMJ Open. 7(11). e017075–e017075. 42 indexed citations

Grob, Jean‐Jacques, Laurent Mortier, Lionel D’Hondt, et al.. (2017). Safety and immunogenicity of MAGE-A3 cancer immunotherapeutic with dacarbazine in patients with MAGE-A3-positive metastatic cutaneous melanoma: an open phase I/II study with a first assessment of a predictive gene signature. ESMO Open. 2(5). e000203–e000203. 15 indexed citations

Curigliano, Giuseppe, Gilles Romieu, Mario Campone, et al.. (2016). A phase I/II trial of the safety and clinical activity of a HER2-protein based immunotherapeutic for treating women with HER2-positive metastatic breast cancer. Breast Cancer Research and Treatment. 156(2). 301–310. 41 indexed citations

Lehmann, Frédéric. (2016). A Factor Graph Approach to Iterative Channel Estimation, Detection, and Decoding for Two-Path Successive Relay Networks. IEEE Transactions on Wireless Communications. 15(8). 5414–5429. 12 indexed citations

10.

Pujol, Jean-Louis, Johan Vansteenkiste, Tommaso Martino De Pas, et al.. (2015). Safety and Immunogenicity of MAGE-A3 Cancer Immunotherapeutic with or without Adjuvant Chemotherapy in Patients with Resected Stage IB to III MAGE-A3-Positive Non-Small-Cell Lung Cancer. Journal of Thoracic Oncology. 10(10). 1458–1467. 51 indexed citations

11.

Hamilton, Erika, Kimberly Blackwell, Amy Hobeika, et al.. (2012). Phase I clinical trial of HER2-specific immunotherapy with concomitant HER2 kinase inhibtion. Journal of Translational Medicine. 10(1). 28–28. 78 indexed citations

12.

Louahed, Jamila, Brigitte Dréno, Swann Gaulis, et al.. (2008). Clinical response to the MAGE-A3 immunotherapeutic in metastatic melanoma patients is associated with a specific gene profile present prior to treatment. Annals of Oncology. 19. 27. 8 indexed citations

13.

Vansteenkiste, Johan, A. Linder, Emilio Esteban, et al.. (2007). B1-05: Activity of MAGE-A3 cancer immunotherapeutic as adjuvant therapy in stage IB/II non-small cell lung cancer (NSCLC): final results of a multi-center, double-blind, randomized, placebo-controlled phase II study. Journal of Thoracic Oncology. 2(8). S334–S335. 4 indexed citations

14.

Lacombe, Denis, P. Fumoleau, H. Zwierzina, et al.. (2002). The EORTC and drug development. European Journal of Cancer. 38. 19–23. 16 indexed citations

15.

Lewalle, Philippe, Rédouane Rouas, Frédéric Lehmann, & Philippe Martiat. (2000). Freezing of dendritic cells, generated from cryopreserved leukaphereses, does not influence their ability to induce antigen-specific immune responses or functionally react to maturation stimuli. Journal of Immunological Methods. 240(1-2). 69–78. 45 indexed citations

16.

Ikeda, Hideyuki, Bernard Lethé, Frédéric Lehmann, et al.. (1997). Characterization of an Antigen That Is Recognized on a Melanoma Showing Partial HLA Loss by CTL Expressing an NK Inhibitory Receptor. Immunity. 6(2). 199–208. 639 indexed citations breakdown →

17.

Zarour, Hassane M., Charles De Smet, Frédéric Lehmann, et al.. (1996). The Majority of Autologous Cytolytic T-Lymphocyte Clones Derived from Peripheral Blood Lymphocytes of a Melanoma Patient Recognize an Antigenic Peptide Derived from Gene Pmel17/gp100. Journal of Investigative Dermatology. 107(1). 63–67. 47 indexed citations

18.

Coulie, Pierre G., P. Weynants, Frédéric Lehmann, et al.. (1993). Genes Coding for Tumor Antigens Recognized by Human Cytolytic T Lymphocytes. Journal of Immunotherapy. 14(2). 104–109. 42 indexed citations

19.

Houssiau, Frédéric, Jean‐Christophe Renauld, Michael J. Stevens, et al.. (1993). Human T cell lines and clones respond to IL-9. The Journal of Immunology. 150(7). 2634–2640. 42 indexed citations

20.

Coulie, Pierre G., Frédéric Lehmann, Philippe Hainaut, et al.. (1992). Precursor frequency analysis of human cytolytic T lymphocytes directed against autologous melanoma cells. International Journal of Cancer. 50(2). 289–297. 110 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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