Hans J. de Haard

2.9k total citations · 1 hit paper
15 papers, 2.3k citations indexed

About

Hans J. de Haard is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology and Oncology. According to data from OpenAlex, Hans J. de Haard has authored 15 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Molecular Biology and 4 papers in Oncology. Recurrent topics in Hans J. de Haard's work include Monoclonal and Polyclonal Antibodies Research (12 papers), Glycosylation and Glycoproteins Research (9 papers) and T-cell and B-cell Immunology (3 papers). Hans J. de Haard is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (12 papers), Glycosylation and Glycoproteins Research (9 papers) and T-cell and B-cell Immunology (3 papers). Hans J. de Haard collaborates with scholars based in Netherlands, Belgium and United States. Hans J. de Haard's co-authors include Michiel M. Harmsen, Rob C. Roovers, Toon Laeremans, Adriaan P. de Bruı̈ne, Anneke W. Reurs, Paula Henderikx, Hennie R. Hoogenboom, Simon E. Hufton, Jan‐Willem Arends and Arie J. Verkleij and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and PLoS ONE.

In The Last Decade

Hans J. de Haard

15 papers receiving 2.2k citations

Hit Papers

Properties, production, and applications of camelid singl... 2007 2026 2013 2019 2007 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hans J. de Haard Netherlands 13 1.6k 1.4k 637 435 160 15 2.3k
Christoph M. Hammers Germany 18 1.8k 1.1× 1.7k 1.2× 815 1.3× 405 0.9× 197 1.2× 56 3.7k
Theo Verrips Netherlands 28 1.1k 0.7× 1.1k 0.8× 536 0.8× 160 0.4× 165 1.0× 47 1.9k
André Frenzel Germany 21 1.1k 0.7× 1.3k 0.9× 448 0.7× 259 0.6× 262 1.6× 50 2.0k
Thomas Schirrmann Germany 31 1.8k 1.1× 1.9k 1.3× 561 0.9× 310 0.7× 425 2.7× 69 2.8k
Weizao Chen United States 26 840 0.5× 1.0k 0.7× 734 1.2× 329 0.8× 70 0.4× 59 2.1k
Sebastian Howe Germany 5 1.2k 0.8× 1.2k 0.9× 679 1.1× 169 0.4× 68 0.4× 6 2.2k
Ricarda Finnern Germany 20 1.1k 0.7× 1.1k 0.8× 466 0.7× 364 0.8× 142 0.9× 30 1.8k
John McCafferty United Kingdom 19 3.3k 2.1× 3.2k 2.3× 775 1.2× 331 0.8× 636 4.0× 24 4.1k
Detlef Güssow Germany 11 1.3k 0.8× 1.4k 1.0× 640 1.0× 206 0.5× 157 1.0× 15 2.1k
Hennie R. Hoogenboom United Kingdom 8 1.8k 1.1× 1.5k 1.1× 490 0.8× 177 0.4× 284 1.8× 9 2.0k

Countries citing papers authored by Hans J. de Haard

Since Specialization
Citations

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

Fields of papers citing papers by Hans J. de Haard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hans J. de Haard. 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 Hans J. de Haard. The network helps show where Hans J. de Haard may publish in the future.

Co-authorship network of co-authors of Hans J. de Haard

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

All Works

15 of 15 papers shown
1.
Stam, Jord C., Steven de Maat, Dorien de Jong, et al.. (2022). Directing HIV-1 for degradation by non-target cells, using bi-specific single-chain llama antibodies. Scientific Reports. 12(1). 13413–13413. 2 indexed citations
2.
Klarenbeek, A., Aline Desmyter, Christophe Blanchetot, et al.. (2015). Camelid Ig V genes reveal significant human homology not seen in therapeutic target genes, providing for a powerful therapeutic antibody platform. mAbs. 7(4). 693–706. 74 indexed citations
3.
Andersen, Jan Terje, María González-Pajuelo, Stian Foss, et al.. (2013). Selection of Nanobodies that Target Human Neonatal Fc Receptor. Scientific Reports. 3(1). 1118–1118. 8 indexed citations
4.
Silence, Karen, Torsten Dreier, Mahan Moshir, et al.. (2013). ARGX-110, a highly potent antibody targeting CD70, eliminates tumors via both enhanced ADCC and immune checkpoint blockade. mAbs. 6(2). 523–532. 76 indexed citations
5.
Hultberg, Anna, Nigel Temperton, Valérie Rosseels, et al.. (2011). Llama-Derived Single Domain Antibodies to Build Multivalent, Superpotent and Broadened Neutralizing Anti-Viral Molecules. PLoS ONE. 6(4). e17665–e17665. 144 indexed citations
6.
Jähnichen, Sven, Christophe Blanchetot, David Maussang, et al.. (2010). CXCR4 nanobodies (VHH-based single variable domains) potently inhibit chemotaxis and HIV-1 replication and mobilize stem cells. Proceedings of the National Academy of Sciences. 107(47). 20565–20570. 198 indexed citations
7.
Tijink, Bernard M., Toon Laeremans, Marianne Budde, et al.. (2008). Improved tumor targeting of anti–epidermal growth factor receptor Nanobodies through albumin binding: taking advantage of modular Nanobody technology. Molecular Cancer Therapeutics. 7(8). 2288–2297. 224 indexed citations
8.
Harmsen, Michiel M. & Hans J. de Haard. (2007). Properties, production, and applications of camelid single-domain antibody fragments. Applied Microbiology and Biotechnology. 77(1). 13–22. 606 indexed citations breakdown →
9.
Klooster, Rinse, Jord C. Stam, Pim Hermans, et al.. (2007). Improved anti-IgG and HSA affinity ligands: Clinical application of VHH antibody technology. Journal of Immunological Methods. 324(1-2). 1–12. 48 indexed citations
10.
Verheesen, Peter, Andreas Roussis, Hans J. de Haard, et al.. (2006). Reliable and controllable antibody fragment selections from Camelid non-immune libraries for target validation. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1764(8). 1307–1319. 55 indexed citations
11.
Roovers, Rob C., Toon Laeremans, Lieven Huang, et al.. (2006). Efficient inhibition of EGFR signalling and of tumour growth by antagonistic anti-EGFR Nanobodies. Cancer Immunology Immunotherapy. 56(3). 303–317. 276 indexed citations
12.
Verheesen, Peter, Anna de Kluijver, Silvana van Koningsbruggen, et al.. (2005). Prevention of oculopharyngeal muscular dystrophy-associated aggregation of nuclear poly(A)-binding protein with a single-domain intracellular antibody. Human Molecular Genetics. 15(1). 105–111. 64 indexed citations
13.
Haard, Hans J. de, Anneke W. Reurs, Simon E. Hufton, et al.. (1999). A Large Non-immunized Human Fab Fragment Phage Library That Permits Rapid Isolation and Kinetic Analysis of High Affinity Antibodies. Journal of Biological Chemistry. 274(26). 18218–18230. 425 indexed citations
14.
Haard, Hans J. de, Bert Kazemier, Arie van der Bent, et al.. (1998). Absolute conservation of residue 6 of immunoglobulin heavy chain variable regions of class IIA is required for correct folding. Protein Engineering Design and Selection. 11(12). 1267–1276. 32 indexed citations
15.
Haard, Hans J. de, et al.. (1993). Gene mapping and expression of two immunodominant Epstein-Barr virus capsid proteins. Journal of Virology. 67(7). 3908–3916. 52 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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