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Nathalie 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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