Support vector machine for functional data classification. - Fabrice Rossi; Nathalie Villa (Neurocomputing, 2006)

Clustering a medieval social network by SOM using a kernel based distance measure. - Nathalie Villa; Romain Boulet (ESANN, 2007)

Various Approaches for Predicting Land Cover in Mountain Areas. - Nathalie Villa; Martin Paegelow; María Teresa Camacho Olmedo; Laurence Cornez; Frédéric Ferraty; Louis Ferré; Pascal Sarda (Commun. Stat. Simul. Comput., 2007)

Consistency of Derivative Based Functional Classifiers on Sampled Data. - Fabrice Rossi; Nathalie Villa (ESANN, 2008)

Batch kernel SOM and related Laplacian methods for social network analysis. - Romain Boulet; Bertrand Jouve; Fabrice Rossi; Nathalie Villa (Neurocomputing, 2008)

Topologically Ordered Graph Clustering via Deterministic Annealing. - Fabrice Rossi; Nathalie Villa (ESANN, 2009)

Identification of non linear multivariable processes modelled on reproducing Kernel Hilbert space: Application to Tennessee process. - Okba Taouali; Ilyes Elaissi; Nathalie Villa; Hassani Messaoud (ICONS, 2009)

A Functional Density-Based Nonparametric Approach for Statistical Calibration. - Noslén Hernández-González; Rolando J. Biscay; Nathalie Villa-Vialaneix; Isneri Talavera-Bustamante (CIARP, 2010)

Optimizing an organized modularity measure for topographic graph clustering: A deterministic annealing approach. - Fabrice Rossi; Nathalie Villa-Vialaneix (Neurocomputing, 2010)

Recherche et représentation de communautés dans un grand graphe. Une approche combinée. - Nathalie Villa-Vialaneix; Taoufiq Dkaki; Sébastien Gadat; Jean-Michel Inglebert; Quoc Dinh Truong (Document Numérique, 2011)

Consistency of functional learning methods based on derivatives. - Fabrice Rossi; Nathalie Villa-Vialaneix (Pattern Recognit. Lett., 2011)

On-Line Relational SOM for Dissimilarity Data. - Madalina Olteanu; Nathalie Villa-Vialaneix; Marie Cottrell (WSOM, 2012)

A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops. - Nathalie Villa-Vialaneix; Marco Follador; Marco Ratto; Adrian Leip (Environ. Model. Softw., 2012)

Neural Networks for Complex Data. - Marie Cottrell; Madalina Olteanu; Fabrice Rossi; Joseph Rynkiewicz; Nathalie Villa-Vialaneix (Künstliche Intell., 2012)

Multiple Kernel Self-Organizing Maps. - Madalina Olteanu; Nathalie Villa-Vialaneix; Christine Cierco-Ayrolles (ESANN, 2013)

Which Dissimilarity Is to Be Used When Extracting Typologies in Sequence Analysis? A Comparative Study. - Sébastien Massoni; Madalina Olteanu; Nathalie Villa-Vialaneix (IWANN (1), 2013)

Data Analysis of Social Simulations Outputs - Interpreting the Dispersion of Variables. - Christophe Sibertin-Blanc; Nathalie Villa-Vialaneix (MABS, 2014)

SOMbrero: An R Package for Numeric and Non-numeric Self-Organizing Maps. - Julien Boelaert; Laura Bendhaiba; Madalina Olteanu; Nathalie Villa-Vialaneix (WSOM, 2014)

Bagged Kernel SOM. - Jérôme Mariette; Madalina Olteanu; Julien Boelaert; Nathalie Villa-Vialaneix (WSOM, 2014)

On-line relational and multiple relational SOM. - Madalina Olteanu; Nathalie Villa-Vialaneix (Neurocomputing, 2015)

System Model Network for Adipose Tissue Signatures Related to Weight Changes in Response to Calorie Restriction and Subsequent Weight Maintenance. - Emilie Montastier; Nathalie Villa-Vialaneix; Sylvie Caspar-Bauguil; Petr Hlavaty; Eva Tvrzicka; Ignacio Gonzalez; Wim H. M. Saris; Dominique Langin; Marie Kunesova; Nathalie Viguerie (PLoS Comput. Biol., 2015)

Beyond multidimensional data in model visualization: High-dimensional and complex nonnumeric data. - Nathalie Villa-Vialaneix; Anne Ruiz-Gazen (Stat. Anal. Data Min., 2015)

Theoretical and Applied Aspects of the Self-Organizing Maps. - Marie Cottrell; Madalina Olteanu; Fabrice Rossi; Nathalie Villa-Vialaneix (WSOM, 2016)

Aggregating Self-Organizing Maps with Topology Preservation. - Jérôme Mariette; Nathalie Villa-Vialaneix (WSOM, 2016)

Sparse Online Self-Organizing Maps for Large Relational Data. - Madalina Olteanu; Nathalie Villa-Vialaneix (WSOM, 2016)

On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection. - Nathalie Villa-Vialaneix; Noslén Hernández; Alain Paris; Céline Domange; Nathalie Priymenko; Philippe Besse (Commun. Stat. Simul. Comput., 2016)

Accelerating stochastic kernel SOM. - Jérôme Mariette; Fabrice Rossi; Madalina Olteanu; Nathalie Villa-Vialaneix (ESANN, 2017)

Stochastic self-organizing map variants with the R package SOMbrero. - Nathalie Villa-Vialaneix (WSOM, 2017)

Random Forests for Big Data. - Robin Genuer; Jean-Michel Poggi; Christine Tuleau-Malot; Nathalie Villa-Vialaneix (Big Data Res., 2017)

Efficient interpretable variants of online SOM for large dissimilarity data. - Jérôme Mariette; Madalina Olteanu; Nathalie Villa-Vialaneix (Neurocomputing, 2017)

Multiple hot-deck imputation for network inference from RNA sequencing data. - Alyssa Imbert; Armand Valsesia; Caroline Le Gall; Claudia Armenise; Gregory Lefebvre; Pierre-Antoine Gourraud; Nathalie Viguerie; Nathalie Villa-Vialaneix (Bioinform., 2018)

Unsupervised multiple kernel learning for heterogeneous data integration. - Jérôme Mariette; Nathalie Villa-Vialaneix (Bioinform., 2018)

Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. - Christophe Ambroise; Alia Dehman; Pierre Neuvial; Guillem Rigaill; Nathalie Villa-Vialaneix (Algorithms Mol. Biol., 2019)

ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra. - Gaëlle Lefort; Laurence Liaubet; Cécile Canlet; Patrick Tardivel; Marie-Christine Père; Hélène Quesnel; Alain Paris; Nathalie Iannuccelli; Nathalie Vialaneix; Rémi Servien (Bioinform., 2019)

Interpretable sparse SIR for functional data. - Victor Picheny; Rémi Servien; Nathalie Villa-Vialaneix (Stat. Comput., 2019)

Applicability and Interpretability of Ward's Hierarchical Agglomerative Clustering With or Without Contiguity Constraints. - Nathanaël Randriamihamison; Nathalie Vialaneix; Pierre Neuvial (J. Classif., 2021)

R-miss-tastic: a unified platform for missing values methods and workflows. - Imke Mayer; Aude Sportisse; Julie Josse; Nicholas J. Tierney; Nathalie Vialaneix (R J., 2022)

Asterics: a simple tool for the ExploRation and Integration of omiCS data. - 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; et al. (BMC Bioinform., 2023)

Should we really use graph neural networks for transcriptomic prediction? - Céline Brouard; Raphaël Mourad; Nathalie Vialaneix (Briefings Bioinform., 2024)

A random forest approach for interval selection in functional regression. - Rémi Servien; Nathalie Vialaneix (Stat. Anal. Data Min., 2024)

A comprehensive review and benchmark of differential analysis tools for Hi-C data. - Elise Jorge; Sylvain Foissac; Pierre Neuvial; Matthias Zytnicki; Nathalie Vialaneix (Briefings Bioinform., 2025)

Phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis. - Camille Guilmineau; Marie Tremblay-Franco; Nathalie Vialaneix; Rémi Servien (BMC Bioinform., 2025)