Jack Kuipers

8.9k total citations
65 papers, 1.3k citations indexed

About

Jack Kuipers is a scholar working on Molecular Biology, Cancer Research and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Jack Kuipers has authored 65 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 22 papers in Cancer Research and 18 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Jack Kuipers's work include Cancer Genomics and Diagnostics (22 papers), Quantum chaos and dynamical systems (17 papers) and Single-cell and spatial transcriptomics (15 papers). Jack Kuipers is often cited by papers focused on Cancer Genomics and Diagnostics (22 papers), Quantum chaos and dynamical systems (17 papers) and Single-cell and spatial transcriptomics (15 papers). Jack Kuipers collaborates with scholars based in Switzerland, Germany and United States. Jack Kuipers's co-authors include Niko Beerenwinkel, Katharina Jahn, Giusi Moffa, Klaus Richter, Gregory Berkolaiko, Daniel Waltner, Jochen Singer, Paul Bebbington, Daniel Freeman and Benjamin J. Raphael and has published in prestigious journals such as Physical Review Letters, Nature Communications and Nature Genetics.

In The Last Decade

Jack Kuipers

64 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jack Kuipers Switzerland 18 609 554 199 163 149 65 1.3k
Jean-Philippe Brunet France 8 796 1.3× 242 0.4× 123 0.6× 63 0.4× 126 0.8× 19 1.6k
Xiaoliang Sunney Xie China 15 1.5k 2.5× 171 0.3× 90 0.5× 73 0.4× 467 3.1× 37 2.1k
Etay Ziv United States 22 390 0.6× 126 0.2× 18 0.1× 128 0.8× 74 0.5× 103 1.7k
Michele Caselle Italy 29 1.0k 1.7× 405 0.7× 344 1.7× 360 2.2× 156 1.0× 160 2.5k
Anandamohan Ghosh India 14 424 0.7× 44 0.1× 140 0.7× 195 1.2× 156 1.0× 39 1.2k
Hua Wu United States 17 546 0.9× 117 0.2× 560 2.8× 7 0.0× 158 1.1× 33 1.7k
Abhinav Nellore United States 21 1.3k 2.2× 565 1.0× 65 0.3× 106 0.7× 145 1.0× 38 2.9k
Bartłomiej Waclaw United Kingdom 20 564 0.9× 224 0.4× 30 0.2× 240 1.5× 368 2.5× 47 1.5k
Chunhe Li China 19 924 1.5× 127 0.2× 27 0.1× 175 1.1× 113 0.8× 67 1.4k
Leo Szilard United States 12 471 0.8× 109 0.2× 101 0.5× 132 0.8× 139 0.9× 26 1.1k

Countries citing papers authored by Jack Kuipers

Since Specialization
Citations

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

Fields of papers citing papers by Jack Kuipers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jack Kuipers

This figure shows the co-authorship network connecting the top 25 collaborators of Jack Kuipers. A scholar is included among the top collaborators of Jack Kuipers 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 Jack Kuipers. Jack Kuipers 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.
Kuipers, Jack, et al.. (2025). DelSIEVE: cell phylogeny modeling of single nucleotide variants and deletions from single-cell DNA sequencing data. Genome biology. 26(1). 255–255. 1 indexed citations
2.
Szczerba, Barbara M., Katharina Jahn, Francesc Castro-Giner, et al.. (2025). Phylogenetic inference reveals clonal heterogeneity in circulating tumor cell clusters. Nature Genetics. 57(6). 1357–1361. 2 indexed citations
3.
Schwede, Matthew, Katharina Jahn, Jack Kuipers, et al.. (2024). Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity. Leukemia. 38(7). 1501–1510. 10 indexed citations
4.
Kuipers, Jack, et al.. (2023). Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG. Journal of Statistical Software. 105(9). 10 indexed citations
5.
Moffa, Giusi, Jack Kuipers, Elizabeth Kuipers, Sally McManus, & Paul Bebbington. (2023). Sexual abuse and psychotic phenomena: a directed acyclic graph analysis of affective symptoms using English national psychiatric survey data. Psychological Medicine. 53(16). 7817–7826. 1 indexed citations
6.
Kuipers, Jack, et al.. (2022). Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks. Briefings in Bioinformatics. 23(4). 10 indexed citations
7.
Kuipers, Jack, Jochen Singer, & Niko Beerenwinkel. (2022). Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence. Bioinformatics. 38(20). 4713–4719. 8 indexed citations
8.
Prummer, Michael, Anıl Tuncel, Ulrike Menzel, et al.. (2022). scAmpi—A versatile pipeline for single-cell RNA-seq analysis from basics to clinics. PLoS Computational Biology. 18(6). e1010097–e1010097. 6 indexed citations
9.
Dazert, Eva, Jack Kuipers, Charlotte K.Y. Ng, et al.. (2022). Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model. PLoS Computational Biology. 18(9). e1009767–e1009767. 10 indexed citations
10.
Dietz, Barbara, et al.. (2021). Universal S-matrix correlations for complex scattering of wave packets in noninteracting many-body systems: Theory, simulation, and experiment. Physical review. E. 103(5). 52209–52209. 3 indexed citations
11.
Moffa, Giusi, Jack Kuipers, Giuseppe Carrà, et al.. (2021). Longitudinal symptomatic interactions in long-standing schizophrenia: a novel five-point analysis based on directed acyclic graphs. Psychological Medicine. 53(4). 1371–1378. 11 indexed citations
12.
Posada-Céspedes, Susana, Gert U. van Zyl, Hesam Montazeri, et al.. (2021). Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C. PLoS Computational Biology. 17(9). e1008363–e1008363. 6 indexed citations
13.
Srivatsa, Sumana, Jack Kuipers, Fabian Schmich, et al.. (2018). Improved pathway reconstruction from RNA interference screens by exploiting off-target effects. Bioinformatics. 34(13). i519–i527. 5 indexed citations
14.
Kuipers, Jack, Giusi Moffa, Jonas Behr, et al.. (2018). Mutational interactions define novel cancer subgroups. Nature Communications. 9(1). 4353–4353. 21 indexed citations
15.
Behr, Jonas, Jochen Singer, Jack Kuipers, et al.. (2017). Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers. BMC Bioinformatics. 18(1). 8–8. 29 indexed citations
16.
Urbina, Juan Diego, et al.. (2016). Multiparticle Correlations in Mesoscopic Scattering: Boson Sampling, Birthday Paradox, and Hong-Ou-Mandel Profiles. Physical Review Letters. 116(10). 100401–100401. 16 indexed citations
17.
Jahn, Katharina, Jack Kuipers, & Niko Beerenwinkel. (2016). Tree inference for single-cell data. Genome biology. 17(1). 86–86. 183 indexed citations
18.
Kuipers, Jack, et al.. (2014). Quantum Graphs Whose Spectra Mimic the Zeros of the Riemann Zeta Function. Physical Review Letters. 112(7). 70406–70406. 4 indexed citations
19.
Kuipers, Jack & Giusi Moffa. (2012). Uniform generation of random acyclic digraphs. arXiv (Cornell University). 2 indexed citations
20.
Waltner, Daniel, et al.. (2009). Semiclassical theory for decay and fragmentation processes in chaotic quantum systems. Physical Review E. 79(4). 46212–46212. 10 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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