Peter A.C. ’t Hoen

46.9k total citations · 2 hit papers
207 papers, 7.9k citations indexed

About

Peter A.C. ’t Hoen is a scholar working on Molecular Biology, Genetics and Physiology. According to data from OpenAlex, Peter A.C. ’t Hoen has authored 207 papers receiving a total of 7.9k indexed citations (citations by other indexed papers that have themselves been cited), including 170 papers in Molecular Biology, 31 papers in Genetics and 21 papers in Physiology. Recurrent topics in Peter A.C. ’t Hoen's work include Muscle Physiology and Disorders (44 papers), RNA Research and Splicing (35 papers) and RNA modifications and cancer (30 papers). Peter A.C. ’t Hoen is often cited by papers focused on Muscle Physiology and Disorders (44 papers), RNA Research and Splicing (35 papers) and RNA modifications and cancer (30 papers). Peter A.C. ’t Hoen collaborates with scholars based in Netherlands, United States and United Kingdom. Peter A.C. ’t Hoen's co-authors include Johan T. den Dunnen, Gert‐Jan B. van Ommen, Henk P.J. Buermans, Eleonora de Klerk, Annemieke Aartsma‐Rus, Yavuz Ariyürek, Marca H. M. Wauben, Esther N. M. Nolte‐‘t Hoen, Willem Stoorvogel and Maaike Waasdorp and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Peter A.C. ’t Hoen

202 papers receiving 7.8k citations

Hit Papers

align trajectories sort by overperformance

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.

Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms

2008 617 citations Peter A.C. ’t Hoen, Renée X. de Menezes et al. Nucleic Acids Research profile →
Deep sequencing of RNA from immune cell-derived vesicles uncovers the selective incorporation of small non-coding RNA biotypes with potential regulatory functions

2012 588 citations Peter A.C. ’t Hoen et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Peter A.C. ’t Hoen Netherlands	47	6.1k	1.2k	1.0k	762	723	207	7.9k
Yiwen Chen United States	43	5.5k 0.9×	1.8k 1.5×	596 0.6×	731 1.0×	337 0.5×	134	6.9k
Alexander R. Pico United States	30	5.7k 0.9×	1.0k 0.9×	655 0.6×	375 0.5×	437 0.6×	57	7.8k
Andrew D. Rouillard United States	12	4.9k 0.8×	1.3k 1.1×	743 0.7×	610 0.8×	302 0.4×	17	8.1k
Kathleen M. Jagodnik United States	17	5.6k 0.9×	1.6k 1.4×	851 0.8×	776 1.0×	265 0.4×	45	9.4k
Sherry L. Jenkins United States	13	5.2k 0.9×	1.4k 1.2×	780 0.8×	705 0.9×	230 0.3×	23	8.4k
Richard B. Pearson Australia	61	9.6k 1.6×	1.3k 1.1×	640 0.6×	922 1.2×	709 1.0×	136	12.4k
Stéphane Richard Canada	67	13.1k 2.1×	1.4k 1.1×	861 0.8×	440 0.6×	322 0.4×	182	15.0k
Gregory W. Gundersen United States	10	4.7k 0.8×	1.3k 1.1×	744 0.7×	626 0.8×	210 0.3×	11	7.6k
Maxim V. Kuleshov United States	12	5.1k 0.8×	1.5k 1.2×	781 0.8×	714 0.9×	230 0.3×	16	8.3k
Alexander Lachmann United States	26	7.9k 1.3×	2.0k 1.7×	1.1k 1.1×	921 1.2×	315 0.4×	46	12.1k

Countries citing papers authored by Peter A.C. ’t Hoen

Since Specialization

Citations

This map shows the geographic impact of Peter A.C. ’t Hoen'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 Peter A.C. ’t Hoen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter A.C. ’t Hoen more than expected).

Fields of papers citing papers by Peter A.C. ’t Hoen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter A.C. ’t Hoen. 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 Peter A.C. ’t Hoen. The network helps show where Peter A.C. ’t Hoen may publish in the future.

Co-authorship network of co-authors of Peter A.C. ’t Hoen

This figure shows the co-authorship network connecting the top 25 collaborators of Peter A.C. ’t Hoen. A scholar is included among the top collaborators of Peter A.C. ’t Hoen 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 Peter A.C. ’t Hoen. Peter A.C. ’t Hoen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wei, Shuai, et al.. (2025). The use of knowledge graphs for drug repurposing: From classical machine learning algorithms to graph neural networks. Computers in Biology and Medicine. 196(Pt C). 110873–110873. 1 indexed citations

Johari, Mridul, Ozan Özışık, Peter A.C. ’t Hoen, et al.. (2025). Multi-omics analysis in inclusion body myositis identifies mir-16 responsible for HLA overexpression. Orphanet Journal of Rare Diseases. 20(1). 27–27. 1 indexed citations

Wilkinson, Mark D., Adrian Tassoni, António Atalaia, et al.. (2025). The FAIR journey of a patient-driven registry: Reflections and practical solutions from the Duchenne Data Platform FAIRification experience. Journal of Neuromuscular Diseases. 708690105–708690105.

Agamah, Francis E., Thomas H. A. Ederveen, Michelle Skelton, et al.. (2024). Network-based Multi-omics Disease–Drug Associations Reveal Drug Repurposing Candidates for Covid-19 Disease Phases. 1(1).

Rigau, Maria, Mattia Bosio, Emily O’Connor, et al.. (2024). Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes. Nature Communications. 15(1). 1227–1227. 12 indexed citations

Cornet, Ronald, Peter A.C. ’t Hoen, Franz Schaefer, et al.. (2022). Towards FAIRification of sensitive and fragmented rare disease patient data: challenges and solutions in European reference network registries. Orphanet Journal of Rare Diseases. 17(1). 436–436. 15 indexed citations

Wang, Eric T., et al.. (2022). A comprehensive atlas of fetal splicing patterns in the brain of adult myotonic dystrophy type 1 patients. NAR Genomics and Bioinformatics. 4(1). lqac016–lqac016. 7 indexed citations

Xue, Li C., et al.. (2022). Entropy and Variability: A Second Opinion by Deep Learning. Biomolecules. 12(12). 1740–1740. 3 indexed citations

Jacobsen, Annika, Martijn G. Kersloot, David van Enckevort, et al.. (2021). The de novo FAIRification process of a registry for vascular anomalies. Orphanet Journal of Rare Diseases. 16(1). 376–376. 11 indexed citations

10.

Rooij, Jeroen van, Pooja R. Mandaviya, Annique Claringbould, et al.. (2019). Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies. Genome biology. 20(1). 235–235. 28 indexed citations

11.

Ouwens, Klaasjan G., Rick Jansen, Michel G. Nivard, et al.. (2019). A characterization of cis- and trans-heritability of RNA-Seq-based gene expression. European Journal of Human Genetics. 28(2). 253–263. 21 indexed citations

12.

Malas, Tareq B., Wouter N. Leonhard, Zoraide Granchi, et al.. (2019). Prioritization of novel ADPKD drug candidates from disease-stage specific gene expression profiles. EBioMedicine. 51. 102585–102585. 20 indexed citations

13.

Yousefi, Soheil, Tooba Abbassi‐Daloii, Thirsa Kraaijenbrink, et al.. (2018). A SNP panel for identification of DNA and RNA specimens. BMC Genomics. 19(1). 90–90. 36 indexed citations

14.

Koorneef, Lisa L., José K. van den Heuvel, Jan Kroon, et al.. (2018). Selective glucocorticoid receptor modulation prevents and reverses non-alcoholic fatty liver disease in male mice. Endocrinology. 159(12). 3925–3936. 46 indexed citations

15.

Malas, Tareq B., et al.. (2017). Semantic Knowledge Graph Network Features for Drug Repurposing.. 1 indexed citations

16.

Pont, Margot J., Edith D. van der Meijden, Cornelis A.M. van Bergen, et al.. (2016). Integrated Whole Genome and Transcriptome Analysis Identified a Therapeutic Minor Histocompatibility Antigen in a Splice Variant of ITGB2. Clinical Cancer Research. 22(16). 4185–4196. 13 indexed citations

17.

Demirkan, Ayşe, Peter Henneman, Aswin Verhoeven, et al.. (2015). Insight in Genome-Wide Association of Metabolite Quantitative Traits by Exome Sequence Analyses. PLoS Genetics. 11(1). e1004835–e1004835. 41 indexed citations

18.

Ivliev, Alexander E., Peter A.C. ’t Hoen, & Marina G. Sergeeva. (2010). Coexpression Network Analysis Identifies Transcriptional Modules Related to Proastrocytic Differentiation and Sprouty Signaling in Glioma. Cancer Research. 70(24). 10060–10070. 79 indexed citations

19.

Hoen, Peter A.C. ’t, Michael Hirsch, Emile J. de Meijer, et al.. (2010). mRNA degradation controls differentiation state-dependent differences in transcript and splice variant abundance. Nucleic Acids Research. 39(2). 556–566. 26 indexed citations

20.

Saris, Jasper J., Peter A.C. ’t Hoen, Ingrid M. Garrelds, et al.. (2006). Prorenin Induces Intracellular Signaling in Cardiomyocytes Independently of Angiotensin II. Hypertension. 48(4). 564–571. 196 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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