David de Graaf

1.4k total citations · 1 hit paper
15 papers, 1.0k citations indexed

About

David de Graaf is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, David de Graaf has authored 15 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Oncology and 6 papers in Computational Theory and Mathematics. Recurrent topics in David de Graaf's work include Computational Drug Discovery Methods (6 papers), Drug Transport and Resistance Mechanisms (4 papers) and Gene Regulatory Network Analysis (3 papers). David de Graaf is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Drug Transport and Resistance Mechanisms (4 papers) and Gene Regulatory Network Analysis (3 papers). David de Graaf collaborates with scholars based in United States, Switzerland and Australia. David de Graaf's co-authors include Jinghai J. Xu, J Chabot, Arthur R. Smith, Peter Henstock, Bart S. Hendriks, Igor B. Roninson, Douglas A. Lauffenburger, Jie Zhao, Neil Kumar and Kevin A. Janes and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Biochemistry and FEBS Letters.

In The Last Decade

David de Graaf

15 papers receiving 998 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.

Cellular Imaging Predictions of Clinical Drug-Induced Liver Injury

2008 378 citations Jinghai J. Xu, Peter Henstock et al. Toxicological Sciences profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David de Graaf United States	11	394	263	240	170	124	15	1.0k
Michael J. Liguori United States	17	455 1.2×	391 1.5×	323 1.3×	213 1.3×	143 1.2×	30	1.3k
Xiao-bo Zhong United States	12	567 1.4×	335 1.3×	274 1.1×	31 0.2×	94 0.8×	13	1.3k
N.R. Kitteringham United Kingdom	20	281 0.7×	345 1.3×	250 1.0×	66 0.4×	48 0.4×	33	971
A Moreau France	19	289 0.7×	217 0.8×	292 1.2×	20 0.1×	78 0.6×	55	1.5k
Robert A.B. van Waterschoot Netherlands	16	332 0.8×	419 1.6×	725 3.0×	56 0.3×	39 0.3×	20	1.2k
Haw-Jyh Chiu United States	10	268 0.7×	234 0.9×	384 1.6×	28 0.2×	128 1.0×	16	894
Yang Sai China	17	427 1.1×	297 1.1×	280 1.2×	49 0.3×	77 0.6×	44	1.0k
Jason R. Manro United States	14	420 1.1×	323 1.2×	556 2.3×	29 0.2×	68 0.5×	28	1.3k
Monicah A. Otieno United States	14	303 0.8×	93 0.4×	83 0.3×	37 0.2×	61 0.5×	27	768

Countries citing papers authored by David de Graaf

Since Specialization

Citations

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

Fields of papers citing papers by David de Graaf

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David de Graaf. 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 David de Graaf. The network helps show where David de Graaf may publish in the future.

Co-authorship network of co-authors of David de Graaf

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

All Works

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15 of 15 papers shown

Thomson, Ty M., Reynald Lescarbeau, David A. Drubin, et al.. (2015). Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis. BMC Medical Genomics. 8(1). 26–26. 29 indexed citations

Fryburg, David A., et al.. (2013). Systems diagnostics: anticipating the next generation of diagnostic tests based on mechanistic insight into disease. Drug Discovery Today. 19(2). 108–112. 11 indexed citations

Laifenfeld, Daphna, David A. Drubin, Natalie L. Catlett, et al.. (2011). Early Patient Stratification and Predictive Biomarkers in Drug Discovery and Development. Advances in experimental medicine and biology. 736. 645–653. 16 indexed citations

Hoeng, Julia, Renée Deehan, Dexter Pratt, et al.. (2011). A network-based approach to quantifying the impact of biologically active substances. Drug Discovery Today. 17(9-10). 413–418. 64 indexed citations

Espelin, Christopher W., et al.. (2010). Elevated GM-CSF and IL-1β levels compromise the ability of p38MAPKinhibitors to modulate TNFα levels in the human monocytic/macrophage U937 cell line. Molecular BioSystems. 6(10). 1956–1972. 10 indexed citations

Espelin, Christopher W., et al.. (2010). Elevated GM-CSF and IL-1beta levels compromise the ability of p38 MAPK inhibitors to modulate TNFalpha levels in the human monocytic/macrophage U937 cell line.. PubMed. 6(10). 1956–72. 4 indexed citations

Xu, Jinghai J., Bart S. Hendriks, Jie Zhao, & David de Graaf. (2008). Multiple effects of acetaminophen and p38 inhibitors: Towards pathway toxicology. FEBS Letters. 582(8). 1276–1282. 75 indexed citations

Xu, Jinghai J., et al.. (2008). Cellular Imaging Predictions of Clinical Drug-Induced Liver Injury. Toxicological Sciences. 105(1). 97–105. 378 indexed citations breakdown →

Amoutzias, Grigoris D., David de Graaf, Anastasia Imsiridou, et al.. (2007). A protein interaction atlas for the nuclear receptors: properties and quality of a hub-based dimerisation network. BMC Systems Biology. 1(1). 34–34. 36 indexed citations

10.

Kumar, Neil, Bart S. Hendriks, Kevin A. Janes, David de Graaf, & Douglas A. Lauffenburger. (2006). Applying computational modeling to drug discovery and development. Drug Discovery Today. 11(17-18). 806–811. 91 indexed citations

11.

Chen, Chang‐Zheng, Li Min, David de Graaf, et al.. (2002). Identification of endoglin as a functional marker that defines long-term repopulating hematopoietic stem cells. Proceedings of the National Academy of Sciences. 99(24). 15468–15473. 140 indexed citations

12.

Roninson, Igor B., et al.. (1998). [16] Isolation of altered-function mutants and genetic suppressor elements of multidrug transporter P-glycoprotein by expression selection from retroviral libraries. Methods in enzymology on CD-ROM/Methods in enzymology. 292. 225–248. 2 indexed citations

13.

Norris, Murray D., David de Graaf, Michelle Haber, et al.. (1996). Involvement ofMDR1 P-glycoprotein in multifactorial resistance to methotrexate. International Journal of Cancer. 65(5). 613–619. 70 indexed citations

14.

Norris, Murray D., David de Graaf, Michelle Haber, et al.. (1996). Involvement of MDR1 P‐glycoprotein in multifactorial resistance to methotrexate. International Journal of Cancer. 65(5). 613–619. 2 indexed citations

15.

Tang‐Wai, David F., Shama Kajiji, Frank M. DiCapua, et al.. (1995). Human (MDR1) and Mouse (mdr1,mdr3) P-glycoproteins Can Be Distinguished by Their Respective Drug Resistance Profiles and Sensitivity to Modulators. Biochemistry. 34(1). 32–39. 98 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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