J.A.L. van Kan

19.9k total citations · 4 hit papers
145 papers, 13.1k citations indexed

About

J.A.L. van Kan is a scholar working on Plant Science, Molecular Biology and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, J.A.L. van Kan has authored 145 papers receiving a total of 13.1k indexed citations (citations by other indexed papers that have themselves been cited), including 122 papers in Plant Science, 56 papers in Molecular Biology and 42 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in J.A.L. van Kan's work include Plant-Microbe Interactions and Immunity (61 papers), Fungal Plant Pathogen Control (41 papers) and Plant Disease Resistance and Genetics (41 papers). J.A.L. van Kan is often cited by papers focused on Plant-Microbe Interactions and Immunity (61 papers), Fungal Plant Pathogen Control (41 papers) and Plant Disease Resistance and Genetics (41 papers). J.A.L. van Kan collaborates with scholars based in Netherlands, United States and United Kingdom. J.A.L. van Kan's co-authors include Arjen ten Have, Paul Tudzynski, B. Williamson, Gary D. Foster, Bettina Tudzynski, K. E. Hammond‐Kosack, Pietro D. Spanu, Regine Kahmann, Antonio Di Pietro and Ralph A. Dean and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

J.A.L. van Kan

140 papers receiving 12.8k citations

Hit Papers

The Top 10 fungal pathoge... 2006 2026 2012 2019 2012 2007 2006 2019 1000 2.0k 3.0k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
J.A.L. van Kan 11.3k 4.4k 4.1k 2.6k 769 145 13.1k
K. E. Hammond‐Kosack 14.2k 1.3× 4.5k 1.0× 4.9k 1.2× 989 0.4× 663 0.9× 155 16.1k
Gary J. Samuels 8.5k 0.8× 7.1k 1.6× 2.8k 0.7× 1.8k 0.7× 706 0.9× 228 10.7k
Susan P. McCormick 10.0k 0.9× 4.7k 1.1× 5.0k 1.2× 1.2k 0.4× 416 0.5× 219 11.7k
Bart P. H. J. Thomma 20.8k 1.8× 6.0k 1.4× 6.9k 1.7× 1.8k 0.7× 2.2k 2.9× 209 23.6k
Paul Tudzynski 6.3k 0.6× 2.5k 0.6× 3.9k 0.9× 3.0k 1.1× 376 0.5× 130 9.0k
Regine Kahmann 11.4k 1.0× 4.7k 1.1× 10.2k 2.5× 1.0k 0.4× 625 0.8× 175 17.2k
Karl‐Heinz Kogel 10.2k 0.9× 2.8k 0.6× 3.4k 0.8× 885 0.3× 820 1.1× 164 11.8k
Harold Kistler 11.7k 1.0× 9.3k 2.1× 3.2k 0.8× 1.0k 0.4× 344 0.4× 113 13.0k
M. L. Gullino 9.3k 0.8× 5.4k 1.2× 1.7k 0.4× 1.1k 0.4× 611 0.8× 717 10.9k
Gary D. Foster 8.0k 0.7× 2.2k 0.5× 3.0k 0.7× 765 0.3× 921 1.2× 143 10.3k

Countries citing papers authored by J.A.L. van Kan

Since Specialization
Citations

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

Fields of papers citing papers by J.A.L. van Kan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J.A.L. van Kan. 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 J.A.L. van Kan. The network helps show where J.A.L. van Kan may publish in the future.

Co-authorship network of co-authors of J.A.L. van Kan

This figure shows the co-authorship network connecting the top 25 collaborators of J.A.L. van Kan. A scholar is included among the top collaborators of J.A.L. van Kan 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 J.A.L. van Kan. J.A.L. van Kan 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
3.
Li, Min, Lei Ji, Xiaowen Fu, et al.. (2025). Identification, Genome Characterization, and Growth Optimization of Paenibacillus peoriae MHJL1 for Biocontrol and Growth Promotion of Cotton Seedlings. Microorganisms. 13(2). 261–261. 1 indexed citations
4.
Kan, J.A.L. van, et al.. (2024). Baking bad: plants in a toasty world with necrotrophs. New Phytologist. 243(6). 2066–2072. 2 indexed citations
5.
Vallée, Amélie de, Christine Rascle, François‐Xavier Gillet, et al.. (2024). A LysM Effector Mediates Adhesion and Plant Immunity Suppression in the Necrotrophic Fungus Botrytis cinerea. Journal of Basic Microbiology. 65(5). e2400552–e2400552. 1 indexed citations
6.
Zhu, Pinkuan, Tobias Mueller, Frederik Sommer, et al.. (2024). Genome Comparisons between Botrytis fabae and the Closely Related Gray Mold Fungus Botrytis cinerea Reveal Possible Explanations for Their Contrasting Host Ranges. Journal of Fungi. 10(3). 216–216. 3 indexed citations
7.
Kan, J.A.L. van, et al.. (2024). Evaluation of cell death-inducing activity of Monilinia spp. effectors in several plants using a modified TRV expression system. Frontiers in Plant Science. 15. 1428613–1428613. 1 indexed citations
8.
Veloso, Javier, et al.. (2023). Molecular characterization of cross-kingdom RNA interference in Botrytis cinerea by tomato small RNAs. Frontiers in Plant Science. 14. 1107888–1107888. 4 indexed citations
9.
Fouillen, Laëtitia, Thomas Leisen, Isabell Albert, et al.. (2022). Cytotoxic activity of Nep1‐like proteins on monocots. New Phytologist. 235(2). 690–700. 13 indexed citations
10.
Silva, Christian J., Sivakumar Pattathil, Lisha Zhang, et al.. (2022). Botrytis cinerea infection accelerates ripening and cell wall disassembly to promote disease in tomato fruit. PLANT PHYSIOLOGY. 191(1). 575–590. 32 indexed citations
11.
Ieperen, W. van, et al.. (2021). Red light imaging for programmed cell death visualization and quantification in plant–pathogen interactions. Molecular Plant Pathology. 22(3). 361–372. 23 indexed citations
12.
Marcet‐Houben, Marina, María Villarino, Laura Vilanova, et al.. (2021). Comparative Genomics Used to Predict Virulence Factors and Metabolic Genes among Monilinia Species. Journal of Fungi. 7(6). 464–464. 14 indexed citations
13.
Zhang, Lisha, Chenlei Hua, Rory N. Pruitt, et al.. (2021). Distinct immune sensor systems for fungal endopolygalacturonases in closely related Brassicaceae. Nature Plants. 7(9). 1254–1263. 46 indexed citations
14.
Vilanova, Laura, Claudio A. Valero‐Jiménez, & J.A.L. van Kan. (2021). Deciphering the Monilinia fructicola Genome to Discover Effector Genes Possibly Involved in Virulence. Genes. 12(4). 568–568. 27 indexed citations
15.
16.
Wan, Wei‐Lin, Lisha Zhang, Rory N. Pruitt, et al.. (2018). Comparing Arabidopsis receptor kinase and receptor protein‐mediated immune signaling reveals BIK1‐dependent differences. New Phytologist. 221(4). 2080–2095. 60 indexed citations
17.
Derbyshire, Mark C., Matthew Denton‐Giles, Dwayne D. Hegedus, et al.. (2017). The Complete Genome Sequence of the Phytopathogenic Fungus Sclerotinia sclerotiorum Reveals Insights into the Genome Architecture of Broad Host Range Pathogens. Genome Biology and Evolution. 9(3). 593–618. 153 indexed citations
18.
Dean, Ralph A., J.A.L. van Kan, Z. A. Pretorius, et al.. (2012). The Top 10 fungal pathogens in molecular plant pathology. Molecular Plant Pathology. 13(4). 414–430. 3274 indexed citations breakdown →
19.
Ackerveken, Guido Van den, J.A.L. van Kan, & P.J.G.M. de Wit. (1992). Molecular analysis of the avirulence gene avr9 of the fungal tomato pathogen Cladosporium fulvum fully supports the gene-for-gene hypothesis. The Plant Journal. 2(3). 359–366. 9 indexed citations
20.
Kan, J.A.L. van, et al.. (1988). The synthesis and possible functions of virus-induced proteins in plants.. PubMed. 5(2). 47–52. 26 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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