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ORCIDGuillem Rigaill
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2020 – today
- 2023
[j19]Martina Sundqvist
, Julien Chiquet, Guillem Rigaill:
Adjusting the adjusted Rand Index. Comput. Stat. 38(1): 327-347 (2023)- [j18]Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill:
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics. J. Mach. Learn. Res. 24: 81:1-81:36 (2023) - [j17]Vincent Runge, Toby Dylan Hocking, Gaetano Romano, Fatemeh Afghah, Paul Fearnhead, Guillem Rigaill:
gfpop: An R Package for Univariate Graph-Constrained Change-Point Detection. J. Stat. Softw. 106(6) (2023) - 2022
- [j16]Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque:
Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data. J. Stat. Softw. 101(10) (2022) - 2021
- [j15]Arnaud Liehrmann, Guillem Rigaill, Toby Dylan Hocking:
Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models. BMC Bioinform. 22(1): 323 (2021) - 2020
- [b2]Guillem Rigaill:
Quelques développements statistiques et algorithmiques pour l'analyse de données génomiques. University of Paris-Saclay, France, 2020 - [j14]Audrey Hulot, Julien Chiquet, Florence Jaffrézic, Guillem Rigaill:
Fast tree aggregation for consensus hierarchical clustering. BMC Bioinform. 21(1): 120 (2020) - [j13]Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque:
Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data. J. Mach. Learn. Res. 21: 87:1-87:40 (2020)
2010 – 2019
- 2019
- [j12]Christophe Ambroise, Alia Dehman, Pierre Neuvial, Guillem Rigaill, Nathalie Villa-Vialaneix:
Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. Algorithms Mol. Biol. 14(1): 22:1-22:14 (2019) - 2018
- [j11]Guillem Rigaill, Sandrine Balzergue, Véronique Brunaud, Eddy Blondet, Andrea Rau, Odile Rogier, José Caius, Cathy Maugis-Rabusseau, Ludivine Taconnat, Sébastien Aubourg, Claire Lurin, Marie-Laure Martin-Magniette, Etienne Delannoy:
Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis. Briefings Bioinform. 19(1): 65-76 (2018) - [j10]Alain Celisse, Guillemette Marot, Morgane Pierre-Jean, Guillem Rigaill:
New efficient algorithms for multiple change-point detection with reproducing kernels. Comput. Stat. Data Anal. 128: 200-220 (2018) - 2017
- [j9]Robert Maidstone, Toby Hocking, Guillem Rigaill, Paul Fearnhead:
On optimal multiple changepoint algorithms for large data. Stat. Comput. 27(2): 519-533 (2017) - 2015
- [j8]Morgane Pierre-Jean, Guillem Rigaill, Pierre Neuvial:
Performance evaluation of DNA copy number segmentation methods. Briefings Bioinform. 16(4): 600-615 (2015) - [j7]Rim Zaag, Jean-Philippe Tamby, Cécile Guichard, Zakia Tariq, Guillem Rigaill, Etienne Delannoy, Jean-Pierre Renou, Sandrine Balzergue, Tristan Mary-Huard, Sébastien Aubourg, Marie-Laure Martin-Magniette, Véronique Brunaud:
GEM2Net: from gene expression modeling to -omics networks, a new CATdb module to investigate Arabidopsis thaliana genes involved in stress response. Nucleic Acids Res. 43(Database-Issue): 1010-1017 (2015) - [c4]Toby Hocking, Guillem Rigaill, Guillaume Bourque:
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data. ICML 2015: 324-332 - 2014
- [j6]Alice Cleynen, Michel Koskas, Emilie Lebarbier, Guillem Rigaill, Stéphane Robin:
Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data. Algorithms Mol. Biol. 9: 6 (2014) - [j5]Toby Dylan Hocking, Valentina Boeva, Guillem Rigaill, Gudrun Schleiermacher, Isabelle Janoueix-Lerosey, Olivier Delattre, Wilfrid Richer, Franck Bourdeaut, Miyuki Suguro, Masao Seto, Francis R. Bach, Jean-Philippe Vert:
SegAnnDB: interactive Web-based genomic segmentation. Bioinform. 30(11): 1539-1546 (2014) - [j4]Alice Cleynen, The Minh Luong, Guillem Rigaill, Grégory Nuel:
Fast estimation of the Integrated Completed Likelihood criterion for change-point detection problems with applications to Next-Generation Sequencing data. Signal Process. 98: 233-242 (2014) - 2013
- [c3]Guillem Rigaill, Vincent Miele, Franck Picard:
Fast and Parallel Algorithm for Population-Based Segmentation of Copy-Number Profiles. CIBB 2013: 248-258 - [c2]Toby Hocking, Guillem Rigaill, Jean-Philippe Vert, Francis R. Bach:
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression. ICML (3) 2013: 172-180 - 2012
- [j3]Guillem Rigaill, Sidney Cadot, Roelof J. C. Kluin, Zheng Xue, René Bernards, Ian J. Majewski, Lodewyk F. A. Wessels:
A regression model for estimating DNA copy number applied to capture sequencing data. Bioinform. 28(18): 2357-2365 (2012) - [j2]Guillem Rigaill, Emilie Lebarbier, Stéphane Robin:
Exact posterior distributions and model selection criteria for multiple change-point detection problems. Stat. Comput. 22(4): 917-929 (2012) - 2010
- [b1]Guillem Rigaill:
Développements statistiques et algorithmiques pour l'analyse des cancers du sein de type triple négatif. (Statistical and algorithmic developments for the analysis of Triple Negative Breast Cancers). AgroParisTech, France, 2010 - [c1]Guillem Rigaill, Emilie Lebarbier, Stéphane Robin:
Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems. COMPSTAT 2010: 557-564
2000 – 2009
- 2008
- [j1]Guillem Rigaill, Philippe Hupé, Anna Almeida, Philippe La Rosa, Jean-Philippe Meyniel, Charles Decraene, Emmanuel Barillot:
ITALICS: an algorithm for normalization and DNA copy number calling for Affymetrix SNP arrays. Bioinform. 24(6): 768-774 (2008)
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