Leo van Iersel

2.2k total citations
61 papers, 953 citations indexed

About

Leo van Iersel is a scholar working on Molecular Biology, Genetics and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Leo van Iersel has authored 61 papers receiving a total of 953 indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Molecular Biology, 38 papers in Genetics and 12 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Leo van Iersel's work include Genomics and Phylogenetic Studies (46 papers), Genetic diversity and population structure (23 papers) and Genome Rearrangement Algorithms (18 papers). Leo van Iersel is often cited by papers focused on Genomics and Phylogenetic Studies (46 papers), Genetic diversity and population structure (23 papers) and Genome Rearrangement Algorithms (18 papers). Leo van Iersel collaborates with scholars based in Netherlands, United Kingdom and New Zealand. Leo van Iersel's co-authors include Steven Kelk, Leen Stougie, Vincent Moulton, Alexander Schönhuth, Gunnar W. Klau, Tobias Marschall, Murray Patterson, Nadia Pisanti, Céline Scornavacca and Mike Steel and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Trends in Genetics.

In The Last Decade

Leo van Iersel

55 papers receiving 914 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Leo van Iersel Netherlands 17 754 586 179 175 164 61 953
Francesc Rosselló Spain 15 553 0.7× 303 0.5× 165 0.9× 278 1.6× 202 1.2× 67 1.0k
Oliver Eulenstein United States 19 876 1.2× 594 1.0× 160 0.9× 215 1.2× 118 0.7× 69 1.0k
Vincent Berry France 19 665 0.9× 464 0.8× 246 1.4× 244 1.4× 151 0.9× 46 1.1k
Steven Kelk Netherlands 15 552 0.7× 419 0.7× 117 0.7× 97 0.6× 107 0.7× 55 707
Glenn Hickey United States 14 656 0.9× 399 0.7× 77 0.4× 348 2.0× 103 0.6× 28 1.0k
Katherine St. John United States 14 440 0.6× 351 0.6× 103 0.6× 94 0.5× 144 0.9× 36 623
Christopher A. Meacham United States 14 389 0.5× 284 0.5× 280 1.6× 231 1.3× 215 1.3× 18 872
Cuong Than United States 8 626 0.8× 551 0.9× 214 1.2× 191 1.1× 115 0.7× 14 844
Aaron Steele United States 7 315 0.4× 268 0.5× 88 0.5× 97 0.6× 30 0.2× 10 702
Dannie Durand United States 17 1.1k 1.4× 443 0.8× 125 0.7× 504 2.9× 34 0.2× 37 1.5k

Countries citing papers authored by Leo van Iersel

Since Specialization
Citations

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

Fields of papers citing papers by Leo van Iersel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leo van Iersel. 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 Leo van Iersel. The network helps show where Leo van Iersel may publish in the future.

Co-authorship network of co-authors of Leo van Iersel

This figure shows the co-authorship network connecting the top 25 collaborators of Leo van Iersel. A scholar is included among the top collaborators of Leo van Iersel 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 Leo van Iersel. Leo van Iersel 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
1.
Iersel, Leo van, et al.. (2025). A simple 4-approximation algorithm for maximum agreement forests on multiple unrooted binary trees. Information Processing Letters. 190. 106572–106572.
2.
Iersel, Leo van, et al.. (2025). Reconstructing semi-directed level-1 networks using few quarnets. Journal of Computer and System Sciences. 152. 103655–103655. 4 indexed citations
3.
Huber, Katharina T., et al.. (2025). Squirrel : Reconstructing Semi-directed Phylogenetic Level-1 Networks from Four-Leaved Networks or Sequence Alignments. Molecular Biology and Evolution. 42(4). 5 indexed citations
4.
Huber, Katharina T., et al.. (2025). When are Quarnets Sufficient to Reconstruct Semi-directed Phylogenetic Networks?. Bulletin of Mathematical Biology. 87(10). 136–136.
5.
Iersel, Leo van. (2024). Faster method for estimating the openness of species. 1 indexed citations
6.
Iersel, Leo van, et al.. (2023). Constructing phylogenetic networks via cherry picking and machine learning. Algorithms for Molecular Biology. 18(1). 13–13. 3 indexed citations
7.
Iersel, Leo van, et al.. (2022). Orchard Networks are Trees with Additional Horizontal Arcs. Bulletin of Mathematical Biology. 84(8). 76–76. 11 indexed citations
8.
Iersel, Leo van, et al.. (2021). A unifying characterization of tree-based networks and orchard networks using cherry covers. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands. 7 indexed citations
9.
Iersel, Leo van, et al.. (2019). Deciding the existence of a cherry-picking sequence is hard on two trees. Discrete Applied Mathematics. 260. 131–143. 4 indexed citations
10.
Iersel, Leo van, et al.. (2019). Reconstructing Tree-Child Networks from Reticulate-Edge-Deleted Subnetworks. Bulletin of Mathematical Biology. 81(10). 3823–3863. 12 indexed citations
11.
Gambette, Philippe, Leo van Iersel, Mark Jones, et al.. (2017). Rearrangement moves on rooted phylogenetic networks. PLoS Computational Biology. 13(8). e1005611–e1005611. 12 indexed citations
12.
Iersel, Leo van, et al.. (2017). On Unrooted and Root-Uncertain Variants of Several Well-Known Phylogenetic Network Problems. Algorithmica. 80(11). 2993–3022. 15 indexed citations
13.
Iersel, Leo van, Steven Kelk, & Céline Scornavacca. (2016). Kernelizations for the hybridization number problem on multiple nonbinary trees. Journal of Computer and System Sciences. 82(6). 1075–1089. 10 indexed citations
14.
Wu, Taoyang, et al.. (2016). TriLoNet: Piecing Together Small Networks to Reconstruct Reticulate Evolutionary Histories. Molecular Biology and Evolution. 33(8). 2151–2162. 19 indexed citations
15.
Gambette, Philippe, Leo van Iersel, Steven Kelk, Fabio Pardi, & Céline Scornavacca. (2016). Do Branch Lengths Help to Locate a Tree in a Phylogenetic Network?. Bulletin of Mathematical Biology. 78(9). 1773–1795. 7 indexed citations
16.
Huber, Katharina T., Leo van Iersel, Vincent Moulton, & Taoyang Wu. (2014). How Much Information is Needed to Infer Reticulate Evolutionary Histories?. Systematic Biology. 64(1). 102–111. 19 indexed citations
17.
Bapteste, Éric, Leo van Iersel, Axel Janke, et al.. (2013). Networks: expanding evolutionary thinking. Trends in Genetics. 29(8). 439–441. 127 indexed citations
18.
Gutin, Gregory, Leo van Iersel, Matthias Mnich, & Anders Yeo. (2011). Every ternary permutation constraint satisfaction problem parameterized above average has a kernel with a quadratic number of variables. Journal of Computer and System Sciences. 78(1). 151–163. 8 indexed citations
19.
Iersel, Leo van & Steven Kelk. (2010). When two trees go to war. Journal of Theoretical Biology. 269(1). 245–255. 19 indexed citations
20.
Iersel, Leo van, et al.. (2009). Constructing Level-2 Phylogenetic Networks from Triplets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 6(4). 667–681. 44 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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