Guillem Rigaill

2.4k total citations
41 papers, 1.4k citations indexed

About

Guillem Rigaill is a scholar working on Molecular Biology, Artificial Intelligence and Plant Science. According to data from OpenAlex, Guillem Rigaill has authored 41 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 7 papers in Artificial Intelligence and 7 papers in Plant Science. Recurrent topics in Guillem Rigaill's work include Gene expression and cancer classification (14 papers), Bioinformatics and Genomic Networks (6 papers) and Genomics and Phylogenetic Studies (5 papers). Guillem Rigaill is often cited by papers focused on Gene expression and cancer classification (14 papers), Bioinformatics and Genomic Networks (6 papers) and Genomics and Phylogenetic Studies (5 papers). Guillem Rigaill collaborates with scholars based in France, United Kingdom and United States. Guillem Rigaill's co-authors include Paul Fearnhead, Thierry Dubois, Anne Vincent‐Salomon, Virginie Maire, Toby Dylan Hocking, Marc‐Henri Stern, Robert Maidstone, Gordon C. Tucker, Francisco Cruzalegui and Étienne Delannoy and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of the American Statistical Association.

In The Last Decade

Guillem Rigaill

40 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guillem Rigaill France 19 791 287 243 193 146 41 1.4k
Yongseok Park United States 14 590 0.7× 171 0.6× 121 0.5× 75 0.4× 59 0.4× 49 1.1k
Weijia Zhang China 24 1.1k 1.4× 217 0.8× 477 2.0× 45 0.2× 65 0.4× 114 2.0k
Ruibin Xi China 22 1.1k 1.4× 307 1.1× 639 2.6× 121 0.6× 215 1.5× 53 2.1k
Marcel Schilling Germany 17 1.4k 1.7× 225 0.8× 102 0.4× 44 0.2× 75 0.5× 31 2.2k
Chao Sima United States 21 907 1.1× 441 1.5× 414 1.7× 43 0.2× 79 0.5× 53 1.9k
Itai Kela Israel 10 1.0k 1.3× 391 1.4× 276 1.1× 88 0.5× 21 0.1× 12 1.4k
Antai Wang China 22 1.1k 1.3× 580 2.0× 397 1.6× 62 0.3× 30 0.2× 74 2.0k
Aldo Solari Italy 16 855 1.1× 517 1.8× 243 1.0× 28 0.1× 291 2.0× 35 1.5k
Yvonne A. Evrard United States 19 1.5k 1.9× 283 1.0× 307 1.3× 119 0.6× 17 0.1× 39 2.1k
Chen Jia China 26 1.1k 1.4× 80 0.3× 118 0.5× 82 0.4× 130 0.9× 126 2.0k

Countries citing papers authored by Guillem Rigaill

Since Specialization
Citations

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

Fields of papers citing papers by Guillem Rigaill

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guillem Rigaill

This figure shows the co-authorship network connecting the top 25 collaborators of Guillem Rigaill. A scholar is included among the top collaborators of Guillem Rigaill 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 Guillem Rigaill. Guillem Rigaill 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.
Delannoy, Étienne, et al.. (2023). DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection. NAR Genomics and Bioinformatics. 5(4). lqad098–lqad098. 2 indexed citations
2.
Chiquet, Julien, et al.. (2022). Adjusting the adjusted Rand Index. Computational Statistics. 38(1). 327–347. 14 indexed citations
3.
Szadkowski, Emmanuel, Véronique Brunaud, Guillem Rigaill, et al.. (2022). Strive or thrive: Trends in Phytophthora capsici gene expression in partially resistant pepper. Frontiers in Plant Science. 13. 980587–980587. 3 indexed citations
4.
5.
Rigaill, Guillem, et al.. (2021). Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models. BMC Bioinformatics. 22(1). 323–323. 3 indexed citations
6.
Torre, Vanesa S. García de la, Clarisse Majorel, Guillem Rigaill, et al.. (2020). Wide cross‐species RNA‐Seq comparison reveals convergent molecular mechanisms involved in nickel hyperaccumulation across dicotyledons. New Phytologist. 229(2). 994–1006. 28 indexed citations
7.
Rigaill, Guillem, et al.. (2020). Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise. arXiv (Cornell University). 19 indexed citations
8.
Chiquet, Julien, et al.. (2020). Fast tree aggregation for consensus hierarchical clustering. BMC Bioinformatics. 21(1). 120–120. 12 indexed citations
9.
Ambroise, Christophe, et al.. (2019). Adjacency-constrained hierarchical clustering of a band similarity\n matrix with application to Genomics. arXiv (Cornell University). 16 indexed citations
10.
Maire, Virginie, Fariba Némati, Laëtitia Lesage, et al.. (2019). Protein arginine methyltransferase 5: A novel therapeutic target for triple‐negative breast cancers. Cancer Medicine. 8(5). 2414–2428. 57 indexed citations
11.
Torkamaneh, Davoud, Guillem Rigaill, Brian Boyle, et al.. (2019). Screening populations for copy number variation using genotyping-by-sequencing: a proof of concept using soybean fast neutron mutants. BMC Genomics. 20(1). 634–634. 4 indexed citations
12.
Rigaill, Guillem, et al.. (2018). Bioinformatic Analysis of Chloroplast Gene Expression and RNA Posttranscriptional Maturations Using RNA Sequencing. Methods in molecular biology. 1829. 279–294. 11 indexed citations
13.
Chiquet, Julien, et al.. (2018). A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer. Methods in molecular biology. 1883. 143–160. 9 indexed citations
14.
Taconnat, Ludivine, Evangelos Barbas, Guillem Rigaill, et al.. (2016). Unraveling the early molecular and physiological mechanisms involved in response to phenanthrene exposure. BMC Genomics. 17(1). 818–818. 21 indexed citations
15.
Maubant, Sylvie, Bruno Tesson, Virginie Maire, et al.. (2015). Transcriptome Analysis of Wnt3a-Treated Triple-Negative Breast Cancer Cells. PLoS ONE. 10(4). e0122333–e0122333. 61 indexed citations
16.
Guichard, Cécile, Guillem Rigaill, Étienne Delannoy, et al.. (2014). GEM2Net: from gene expression modeling to -omics networks, a new CATdb module to investigate Arabidopsis thaliana genes involved in stress response. Nucleic Acids Research. 43(D1). D1010–D1017. 10 indexed citations
17.
Ronde, Jorma J. de, Guillem Rigaill, Sven Rottenberg, Sjoerd Rodenhuis, & Lodewyk F.A. Wessels. (2013). Identifying subgroup markers in heterogeneous populations. Nucleic Acids Research. 41(21). e200–e200. 16 indexed citations
18.
Maire, Virginie, Céline Baldeyron, Marion Richardson, et al.. (2013). TTK/hMPS1 Is an Attractive Therapeutic Target for Triple-Negative Breast Cancer. PLoS ONE. 8(5). e63712–e63712. 119 indexed citations
19.
Cleynen, Alice, et al.. (2012). A Generic Implementation of the Pruned Dynamic Programing Algorithm. arXiv (Cornell University). 4 indexed citations
20.
Maire, Virginie, Eléonore Gravier, Guillem Rigaill, et al.. (2008). Frequent PTEN genomic alterations and activated phosphatidylinositol 3-kinase pathway in basal-like breast cancer cells. Breast Cancer Research. 10(6). R101–R101. 170 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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