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ORCIDNathalie Vialaneix
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2020 – today
- 2024
[j23]Céline Brouard
, Raphaël Mourad, Nathalie Vialaneix:
Should we really use graph neural networks for transcriptomic prediction? Briefings Bioinform. 25(2) (2024)- 2023
- [j22]Élise Maigné, Céline Noirot, Julien Henry, Yaa Adu Kesewaah, Ludovic Badin, Sébastien Déjean, Camille Guilmineau, Arielle Krebs, Fanny Mathevet, Audrey Segalini, Laurent Thomassin, David Colongo, Christine Gaspin, Laurence Liaubet, Nathalie Vialaneix:
Asterics: a simple tool for the ExploRation and Integration of omiCS data. BMC Bioinform. 24(1): 391 (2023) - 2022
- [j21]Imke Mayer, Aude Sportisse, Julie Josse, Nicholas J. Tierney, Nathalie Vialaneix:
R-miss-tastic: a unified platform for missing values methods and workflows. R J. 14(2): 245-267 (2022) - 2021
- [j20]Nathanaël Randriamihamison, Nathalie Vialaneix, Pierre Neuvial:
Applicability and Interpretability of Ward's Hierarchical Agglomerative Clustering With or Without Contiguity Constraints. J. Classif. 38(2): 363-389 (2021)
2010 – 2019
- 2019
- [j19]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) - [j18]Gaëlle Lefort, Laurence Liaubet, Cécile Canlet, Patrick Tardivel, Marie-Christine Père, Hélène Quesnel, Alain Paris, Nathalie Iannuccelli, Nathalie Vialaneix, Rémi Servien:
ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra. Bioinform. 35(21): 4356-4363 (2019) - [j17]Victor Picheny, Rémi Servien, Nathalie Villa-Vialaneix:
Interpretable sparse SIR for functional data. Stat. Comput. 29(2): 255-267 (2019) - 2018
- [j16]Jérôme Mariette, Nathalie Villa-Vialaneix:
Unsupervised multiple kernel learning for heterogeneous data integration. Bioinform. 34(6): 1009-1015 (2018) - [j15]Alyssa Imbert, Armand Valsesia, Caroline Le Gall, Claudia Armenise, Gregory Lefebvre, Pierre-Antoine Gourraud, Nathalie Viguerie, Nathalie Villa-Vialaneix:
Multiple hot-deck imputation for network inference from RNA sequencing data. Bioinform. 34(10): 1726-1732 (2018) - 2017
- [j14]Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot, Nathalie Villa-Vialaneix:
Random Forests for Big Data. Big Data Res. 9: 28-46 (2017) - [j13]Jérôme Mariette, Madalina Olteanu, Nathalie Villa-Vialaneix:
Efficient interpretable variants of online SOM for large dissimilarity data. Neurocomputing 225: 31-48 (2017) - [c17]Jérôme Mariette, Fabrice Rossi, Madalina Olteanu, Nathalie Villa-Vialaneix:
Accelerating stochastic kernel SOM. ESANN 2017 - [c16]Nathalie Villa-Vialaneix:
Stochastic self-organizing map variants with the R package SOMbrero. WSOM 2017: 104-110 - 2016
- [j12]Nathalie Villa-Vialaneix, Noslén Hernández, Alain Paris, Céline Domange, Nathalie Priymenko, Philippe Besse:
On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection. Commun. Stat. Simul. Comput. 45(1): 282-298 (2016) - [c15]Marie Cottrell, Madalina Olteanu, Fabrice Rossi, Nathalie Villa-Vialaneix:
Theoretical and Applied Aspects of the Self-Organizing Maps. WSOM 2016: 3-26 - [c14]Jérôme Mariette, Nathalie Villa-Vialaneix:
Aggregating Self-Organizing Maps with Topology Preservation. WSOM 2016: 27-37 - [c13]Madalina Olteanu, Nathalie Villa-Vialaneix:
Sparse Online Self-Organizing Maps for Large Relational Data. WSOM 2016: 73-82 - 2015
- [j11]Madalina Olteanu, Nathalie Villa-Vialaneix:
On-line relational and multiple relational SOM. Neurocomputing 147: 15-30 (2015) - [j10]Emilie Montastier, Nathalie Villa-Vialaneix, Sylvie Caspar-Bauguil, Petr Hlavaty, Eva Tvrzicka, Ignacio Gonzalez, Wim H. M. Saris, Dominique Langin, Marie Kunesova, Nathalie Viguerie:
System Model Network for Adipose Tissue Signatures Related to Weight Changes in Response to Calorie Restriction and Subsequent Weight Maintenance. PLoS Comput. Biol. 11(1) (2015) - [j9]Nathalie Villa-Vialaneix, Anne Ruiz-Gazen:
Beyond multidimensional data in model visualization: High-dimensional and complex nonnumeric data. Stat. Anal. Data Min. 8(4): 232-239 (2015) - [i3]Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot, Nathalie Villa-Vialaneix:
Random Forests for Big Data. CoRR abs/1511.08327 (2015) - 2014
- [c12]Christophe Sibertin-Blanc, Nathalie Villa-Vialaneix:
Data Analysis of Social Simulations Outputs - Interpreting the Dispersion of Variables. MABS 2014: 133-150 - [c11]Jérôme Mariette, Madalina Olteanu, Julien Boelaert, Nathalie Villa-Vialaneix:
Bagged Kernel SOM. WSOM 2014: 45-54 - [c10]Julien Boelaert, Laura Bendhaiba, Madalina Olteanu, Nathalie Villa-Vialaneix:
SOMbrero: An R Package for Numeric and Non-numeric Self-Organizing Maps. WSOM 2014: 219-228 - 2013
- [c9]Madalina Olteanu, Nathalie Villa-Vialaneix, Christine Cierco-Ayrolles:
Multiple Kernel Self-Organizing Maps. ESANN 2013 - [c8]Sébastien Massoni, Madalina Olteanu, Nathalie Villa-Vialaneix:
Which Dissimilarity Is to Be Used When Extracting Typologies in Sequence Analysis? A Comparative Study. IWANN (1) 2013: 69-79 - 2012
- [j8]Nathalie Villa-Vialaneix, Marco Follador, Marco Ratto, Adrian Leip:
A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops. Environ. Model. Softw. 34: 51-66 (2012) - [j7]Marie Cottrell, Madalina Olteanu, Fabrice Rossi, Joseph Rynkiewicz, Nathalie Villa-Vialaneix:
Neural Networks for Complex Data. Künstliche Intell. 26(4): 373-380 (2012) - [c7]Madalina Olteanu, Nathalie Villa-Vialaneix, Marie Cottrell:
On-Line Relational SOM for Dissimilarity Data. WSOM 2012: 13-22 - [i2]Marie Cottrell, Madalina Olteanu, Fabrice Rossi, Joseph Rynkiewicz, Nathalie Villa-Vialaneix:
Neural Networks for Complex Data. CoRR abs/1210.6511 (2012) - [i1]Madalina Olteanu, Nathalie Villa-Vialaneix, Marie Cottrell:
On-line relational SOM for dissimilarity data. CoRR abs/1212.6316 (2012) - 2011
- [j6]Nathalie Villa-Vialaneix, Taoufiq Dkaki, Sébastien Gadat, Jean-Michel Inglebert, Quoc Dinh Truong:
Recherche et représentation de communautés dans un grand graphe. Une approche combinée. Document Numérique 14(1): 59-80 (2011) - [j5]Fabrice Rossi, Nathalie Villa-Vialaneix:
Consistency of functional learning methods based on derivatives. Pattern Recognit. Lett. 32(8): 1197-1209 (2011) - 2010
- [j4]Fabrice Rossi, Nathalie Villa-Vialaneix:
Optimizing an organized modularity measure for topographic graph clustering: A deterministic annealing approach. Neurocomputing 73(7-9): 1142-1163 (2010) - [c6]Noslén Hernández-González, Rolando J. Biscay, Nathalie Villa-Vialaneix, Isneri Talavera-Bustamante:
A Functional Density-Based Nonparametric Approach for Statistical Calibration. CIARP 2010: 450-457
2000 – 2009
- 2009
- [c5]Fabrice Rossi, Nathalie Villa:
Topologically Ordered Graph Clustering via Deterministic Annealing. ESANN 2009 - [c4]Okba Taouali, Ilyes Elaissi, Nathalie Villa, Hassani Messaoud:
Identification of non linear multivariable processes modelled on reproducing Kernel Hilbert space: Application to Tennessee process. ICONS 2009: 197-202 - 2008
- [j3]Romain Boulet, Bertrand Jouve, Fabrice Rossi, Nathalie Villa:
Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing 71(7-9): 1257-1273 (2008) - [c3]Fabrice Rossi, Nathalie Villa:
Consistency of Derivative Based Functional Classifiers on Sampled Data. ESANN 2008: 445-450 - 2007
- [j2]Nathalie Villa, Martin Paegelow, María Teresa Camacho Olmedo, Laurence Cornez, Frédéric Ferraty, Louis Ferré, Pascal Sarda:
Various Approaches for Predicting Land Cover in Mountain Areas. Commun. Stat. Simul. Comput. 36(1): 73-86 (2007) - [c2]Nathalie Villa, Romain Boulet:
Clustering a medieval social network by SOM using a kernel based distance measure. ESANN 2007: 31-36 - 2006
- [j1]Fabrice Rossi, Nathalie Villa:
Support vector machine for functional data classification. Neurocomputing 69(7-9): 730-742 (2006) - 2005
- [c1]Nathalie Villa, Fabrice Rossi:
Support Vector Machine For Functional Data Classification. ESANN 2005: 467-472
Coauthor Index
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