Publications

Journal articles

  1. Neuvial, P., Randriamihamison, N., Chavent, M., Foissac, S., & Vialaneix, N. (2024). A two-sample tree-based test for hierarchically organized genomic signals. Journal of the Royal Statistical Society, Series C, qlae011.

  2. Servien, R., & Vialaneix, N. (2024). A random forest approach for interval selection in functional regression. Statistical Analysis and Data Mining, 17(4), e11705.

  3. Brouard, C., Mourad, R., & Vialaneix, N. (2024). Should we really use graph neural networks for transcriptomic prediction? Briefings in Bioinformatics, 25(2), bbae027.

  4. Malonga, T., Vialaneix, N., & Beaumont, M. (2024). BEST4+ cells in the intestinal epithelium. American Journal of Physiology – Cell Physiology, 326(5), C1345–C1352. Review article.

  5. Liaubet, L., Guilmineau, C., Lefort, G., Billon, Y., Reigner, S., Bailly, J., Marty-Gasset, N., Gress, L., Servien, R., Bonnet, A., Gilbert, H., Vialaneix, N., & Quesnel, H. (2023). Plasma ^1H-NMR metabolic and amino acid profiles of newborn piglets from two lines divergently selected for residual feed intake. Scientific Reports, 13, 7127.

  6. Maigné, É., Noirot, C., Henry, J., Adu Kesewaah, Y., Badin, L., Déjean, S., Guilmineau, C., Krebs, A., Mathevet, F., Segalini, A., Thomassin, L., Colongo, D., Gaspin, C., Liaubet, L., & Vialaneix, N. (2023). ASTERICS: A Simple Tool for the ExploRation and Integration of omiCS data. BMC Bioinformatics, 24, 391.

  7. Imbert, A., Vialaneix, N., Marquis, J., Vion, J., Charpagne, A., Metairon, S., Laurens, C., Moro, C., Boulet, N., Walter, O., Lefebvre, G., Hager, J., Langin, D., Saris, W. H. M., Astrup, A., Viguerie, N., & Valsesia, A. (2022). Network analyses reveal negative link between changes in adipose tissue GDF15 and BMI during dietary induced weight loss. The Journal of Clinical Endocrinology and Metabolism, 107(1), e130–e142.

  8. Mayer, I., Sportisse, A., Josse, J., Tierney, N., & Vialaneix, N. (2022). R-miss-tastic: a unified platform for missing values methods and workflows. The R Journal, 14(2), 244–266.

  9. Brouard, C., Mariette, J., Flamary, R., & Vialaneix, N. (2022). Feature selection for kernel methods in systems biology. NAR Genomics and Bioinformatics, 4(1), lqac014.

  10. Randriamihamison, N., Vialaneix, N., & Neuvial, P. (2021). Applicability and interpretability of Ward’s hierarchical agglomerative clustering with or without contiguity constraints. Journal of Classification, 38, 363–389.

  11. Lefort, G., Liaubet, L., Marty-Gasset, N., Canlet, C., Vialaneix, N., & Servien, R. (2021). Joint automatic metabolite identification and quantification of a set of ^1H NMR spectra. Analytical Chemistry, 93(5), 2861–2870. Co-last author

  12. Marti-Marimon, M., Vialaneix, N., Lahbib-Mansais, Y., Zytnicki, M., Camut, S., Robelin, D., Yerle-Bouissou, M., & Foissac, S. (2021). Major reorganization of chromosome conformation during muscle development in pig. Frontiers in Genetics, 12, 748239.

  13. Lefort, G., Servien, R., Quesnel, H., Billon, Y., Canario, L., Iannucelli, N., Canlet, C., Paris, A., Vialaneix, N., & Liaubet, L. (2020). The maturity in fetal pigs using a multi-fluid metabolomic approach. Scientific Report, 10, 19912. Co-last author

  14. Foissac, S., Djebali, S., Munyard, K., Vialaneix, N., Rau, A., Muret, K., Esquerre, D., Zytnicki, M., Derrien, T., Bardou, P., Blanc, F., Cabau, C., Crisci, E., Dhorne-Pollet, S., Drouet, F., Faraut, T., Gonzáles, I., Goubil, A., Lacroix-Lamande, S., Laurent, F., … Giuffra, E. (2019). Multi-species annotation of transcriptome and chromatin structure in domesticated animals. BMC Biology, 17, 108.

  15. Picheny, V., Servien, R., & Villa-Vialaneix, N. (2019). Interpretable sparse sliced inverse regression for functional data. Statistics and Computing, 29(2), 255–267.

  16. Ambroise, C., Dehman, A., Neuvial, P., Rigaill, G., & Vialaneix, N. (2019). Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. Algorithms for Molecular Biology, 14, 22.

  17. Lefort, G., Liaubet, L., Canlet, C., Tardivel, P., Père, M.-C., Quesnel, H., Paris, A., Iannuccelli, N., Vialaneix, N., & Servien, R. (2019). ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra. Bioinformatics, 35(21), 4356–4363.

  18. Cottrell, M., Olteanu, M., Rossi, F., & Villa-Vialaneix, N. (2018). Self-organizing maps, theory and applications. Revista Investigacion Operacional, 39(1), 1–22.

  19. Imbert, A., Valsesia, A., Le Gall, C., Armenise, C., Lefebvre, G., Gourraud, P.-A., Viguerie, N., & Villa-Vialaneix, N. (2018). Multiple hot-deck imputation for network inference from RNA sequencing data. Bioinformatics, 34(10), 1726–1732.

  20. Imbert, A., & Vialaneix, N. (2018). Décrire, prendre en compte, imputer et évaluer les valeurs manquantes dans les études statistiques : une revue des approches existantes. Journal De La Société Française De Statistique, 159(2), 1–55. In French

  21. Mariette, J., & Villa-Vialaneix, N. (2018). Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics, 34(6), 1009–1015.

  22. Marti-Marimon, M., Vialaneix, N., Voillet, V., Yerle-Bouissou, M., Lahbib-Mansais, Y., & Liaubet, L. (2018). A new approach of gene co-expression network inference reveals significant biological processes involved in porcine muscle development in late gestation. Scientific Report, 8, 10150.

  23. Bolton, J., Montastier, E., Carayol, J., Bonnel, S., Mir, L., Marques, M.-A., Astrup, A., Saris, W., Iacovoni, J., Villa-Vialaneix, N., Valsesia, A., Langin, D., & Viguerie, N. (2017). Molecular biomarkers for weight control in obese individuals subjected to a multi-phase dietary intervention. The Journal of Clinical Endocrinology and Metabolism, 102(8), 2751–2761.

  24. Dou, S., Villa-Vialaneix, N., Liaubet, L., Billon, Y., Giorgi, M., Gilbert, H., Gourdine, J.-L., Riquet, J., & Renaudeau, D. (2017). ^1H NMR-based metabolomic profiling method to develop plasma biomarkers for sensitivity to chronic heat stress in growing pigs. PLoS ONE, 12(11), e0188469.

  25. Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017). Random forests for big data. Big Data Research, 9, 28–46.

  26. Mariette, J., Olteanu, M., & Vialaneix, N. (2017). Efficient interpretable variants of online SOM for large dissimilarity data. Neurocomputing, 225, 31–48.

  27. Terenina, E., Sautron, V., Ydier, C., Bazovkina, D., Sevin-Pujol, A., Gress, L., Lippi, Y., Naylies, C., Billon, Y., Larzul, C., Liaubet, L., Mormède, P., & Villa-Vialaneix, N. (2017). Time course study of the response to LPS targeting the pig immune response gene networks. BMC Genomics, 18, 988.

  28. Villa-Vialaneix, N., Hernandez, N., Paris, A., Domange, C., Priymenko, N., & Besse, P. (2016). On combining wavelets expansion and sparse linear models for regression on metabolomic data and biomarker selection. Communications in Statistics - Simulation and Computation, 45(1), 282–298.

  29. Bar-Hen, A., Villa-Vialaneix, N., & Javaux, H. (2015). Analyse statistique des profils et de l’activité des participants d’un MOOC. Revue Internationale Des Technologies En Pédagogie Universitaire, 12(1-2), 11–22. In French

  30. Hernández, N., Biscay, R. J., Villa-Vialaneix, N., & Talavera, I. (2015). A non parametric approach for calibration with functional data. Statistica Sinica, 25, 1547–1566.

  31. Montastier, E., Villa-Vialaneix, N., Caspar-Bauguil, S., Hlavaty, P., Tvrzicka, E., Gonzalez, I., Saris, W. H. M., Langin, D., Kunesova, M., & Viguerie, N. (2015). System model network for adipose tissue signatures related to weight changes in response to calorie restriction and subsequent weight maintenance. PLoS Computational Biology, 11(1), e1004047. Co-first author

  32. Olteanu, M., & Villa-Vialaneix, N. (2015). Using SOMbrero for clustering and visualizing graphs. Journal De La Société Française De Statistique, 156(3), 95–119.

  33. Olteanu, M., & Villa-Vialaneix, N. (2015). On-line relational and multiple relational SOM. Neurocomputing, 147, 15–30.

  34. Sautron, V., Terenina, E., Gress, L., Lippi, Y., Billon, Y., Larzul, C., Liaubet, L., Villa-Vialaneix, N., & Mormède, P. (2015). Time course of the response to ACTH in pig: biological and transcriptomic study. BMC Genomics, 16(961), PMC4650497.

  35. Villa-Vialaneix, N., & Ruiz-Gazen, A. (2015). Beyond multi-dimensional data in model visualization: high-dimensional and complex nonnumeric data. Statistical Analysis and Data Mining, 8(4), 232–239. Discussion paper.

  36. Villa-Vialaneix, N., Sibertin-Blanc, C., & Roggero, P. (2014). Statistical exploratory analysis of agent-based simulations in a social context. Case Studies in Business, Industry and Government Statistics, 5(2), 132–149.

  37. Villa-Vialaneix, N., Vignes, M., Viguerie, N., & San Cristobal, M. (2014). Inferring networks from multiple samples with concensus LASSO. Quality Technology and Quantitative Management, 11(1), 39–60.

  38. Rossi, F., Villa-Vialaneix, N., & Hautefeuille, F. (2013). Exploration of a large database of French notarial acts with social network methods. Digital Medievalist, 9.

  39. Villa-Vialaneix, N. (2013). J’ai testé pour vous... un MOOC. Statistique Et Enseignement, 4(2), 3–17. In French

  40. Villa-Vialaneix, N., Liaubet, L., Laurent, T., Cherel, P., Gamot, A., & San Cristobal, M. (2013). The structure of a gene co-expression network reveals biological functions underlying eQTLs. PLoS ONE, 8(4), e60045.

  41. Cottrell, M., Olteanu, M., Rossi, F., Rynkiewicz, J., & Villa-Vialaneix, N. (2012). Neural networks for complex data. Künstliche Intelligenz, 26(2), 1–8.

  42. Rohart, F., Paris, A., Laurent, B., Canlet, C., Molina, J., Mercat, M. J., Tribout, T., Muller, N., Iannuccelli, N., Villa-Vialaneix, N., Liaubet, L., Milan, D., & San Cristobal, M. (2012). Phenotypic prediction based on metabolomic data on the growing pig from three main European breeds. Journal of Animal Science, 90(12), 4729–4740. Article summarized in French for the journal Viandes et Produits Carnés

  43. Viguerie, N., Montastier, E., Maoret, J. J., Roussel, B., Combes, M., Valle, C., Villa-Vialaneix, N., Iacovoni, J. S., Martinez, J. A., Holst, C., Astrup, A., Vidal, H., Clément, K., Hager, J., Saris, W. H. M., & Langin, D. (2012). Determinants of human adipose tissue gene expression: impact of diet, sex, metabolic status and cis genetic regulation. PLoS Genetics, 8(9), e1002959.

  44. Villa-Vialaneix, N., Follador, M., Ratto, M., & Leip, A. (2012). A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops. Environmental Modelling and Software, 34, 51–66.

  45. Villa-Vialaneix, N., Jouve, B., Rossi, F., & Hautefeuille, F. (2012). Spatial correlation in bipartite networks: the impact of the geographical distances on the relations in a corpus of medieval transactions. Revue Des Nouvelles Technologies De l’Information, SHS-1, 97–110.

  46. Hernández, N., Biscay, R. J., Villa-Vialaneix, N., & Talavera, I. (2011). A simulation study of functional density-based inverse regression. Revista Investigacion Operacional, 32(2), 146–159.

  47. Laurent, T., & Villa-Vialaneix, N. (2011). Using spatial indexes for labeled network analysis. Information, Interaction, Intelligence (I3), 11(1).

  48. Rossi, F., & Villa-Vialaneix, N. (2011). Représentation d’un grand réseau à partir d’une classification hiérarchique de ses sommets. Journal De La Société Française De Statistique, 152(3), 34–65. In French

  49. Rossi, F., & Villa-Vialaneix, N. (2011). Consistency of functional learning methods based on derivatives. Pattern Recognition Letters, 32(8), 1197–1209.

  50. Villa-Vialaneix, N., Dkaki, T., Gadat, S., Inglebert, J. M., & Truong, Q. D. (2011). Recherche et représentation de communautés dans un grand graphe : une approche combinée. Document Numérique, 14(1), 59–80. In French

  51. Rossi, F., & Villa-Vialaneix, N. (2010). Optimizing an organized modularity measure for topographic graph clustering: a deterministic annealing approach. Neurocomputing, 73(7-9), 1142–1163.

  52. Boulet, R., Jouve, B., Rossi, F., & Villa, N. (2008). Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing, 71(7-9), 1257–1273. Comments upon this article can be found on Nature web site, Nature News, in Le Figaro, May 28th, 2008 and in the Journal du CNRS

  53. Ruiz-Gazen, A., & Villa, N. (2007). Storms prediction: logistic regression vs random forest for unbalanced data. Case Studies in Business, Industry and Government Statistics, 1(2), 91–101.

  54. Villa, N., Paëgelow, M., Camacho Olmedo, M. T., Cornez, L., Ferraty, F., Ferré, L., & Sarda, P. (2007). Various approaches to predicting land cover in mountain areas. Communication in Statistics - Simulation and Computation, 36(1), 73–86.

  55. Ferré, L., & Villa, N. (2006). Multi-layer perceptron with functional inputs: an inverse regression approach. Scandinavian Journal of Statistics, 33(4), 807–823.

  56. Rossi, F., & Villa, N. (2006). Support vector machine for functional data classification. Neurocomputing, 69(7-9), 730–742.

  57. Villa, N., & Rossi, F. (2006). Un résultat de consistance pour des SVM fonctionnels par interpolation spline. Comptes Rendus Mathématique. Académie Des Sciences. Paris, 343(8), 555–560. In French

  58. Ferré, L., & Villa, N. (2005). Discrimination de courbes par régression inverse fonctionnelle. Revue De Statistique Appliquée, LIII(1), 39–57. In French

  59. Paëgelow, M., Villa, N., Cornez, L., Ferraty, F., Ferré, L., & Sarda, P. (2004). Modélisations prospectives de l’occupation du sol. Le cas d’une montagne méditerranéenne. Cybergéo, 295. In French

International conferences (with peer-reviewed proceedings)

  1. Mariette, J., Rossi, F., Olteanu, M., & Villa-Vialaneix, N. (2017). Accelerating stochastic kernel SOM. In M. Verleysen (Ed.), XXVth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017) (pp. 269–274). Bruges, Belgium: i6doc.

  2. Mariette, J., & Villa-Vialaneix, N. (2016). Aggregating Self-organizing maps with topology preservation. In E. Merényi, M. J. Mendenhall, & O. D. P. (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2016) (Vol. 428, pp. 27–37). Houston, TX, USA: Springer International Publishing Switzerland.

  3. Olteanu, M., & Villa-Vialaneix, N. (2016). Sparse online self-organizing maps for large relational data. In E. Merényi, M. J. Mendenhall, & O. D. P. (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2016) (Vol. 428, pp. 27–37). Houston, TX, USA: Springer International Publishing Switzerland.

  4. Sibertin-Blanc, C., & Villa-Vialaneix, N. (2015). Data analysis of social simulations outputs. In F. Grimaldo & E. Norling (Eds.), Multi-Agent-Based Simulation XV (Proceedings of MABS 2014) (Vol. 9002, pp. 133–150). Paris, France: Springer International Publishing Switzerland.

  5. Boelaert, J., Bendhaïba, L., Olteanu, M., & Villa-Vialaneix, N. (2014). SOMbrero: an R package for numeric and non-numeric self-organizing maps. In T. Villmann, F. M. Schleif, M. Kaden, & M. Lange (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014) (Vol. 295, pp. 219–228). Mittweida, Germany: Springer Verlag, Berlin, Heidelberg.

  6. Mariette, J., Olteanu, M., Boelaert, J., & Villa-Vialaneix, N. (2014). Bagged kernel SOM. In T. Villmann, F. M. Schleif, M. Kaden, & M. Lange (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014) (Vol. 295, pp. 45–54). Mittweida, Germany: Springer Verlag, Berlin, Heidelberg.

  7. Massoni, S., Olteanu, M., & Villa-Vialaneix, N. (2013). Which distance use when extracting typologies in sequence analysis? An application to school to work transitions. In International Work Conference on Artificial Neural Networks (IWANN 2013). Puerto de la Cruz, Tenerife.

  8. Olteanu, M., Villa-Vialaneix, N., & Cierco-Ayrolles, C. (2013). Multiple kernel self-organizing maps. In M. Verleysen (Ed.), XXIst European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013) (pp. 83–88). Bruges, Belgium: i6doc.com.

  9. Olteanu, M., Villa-Vialaneix, N., & Cottrell, M. (2013). On-line relational SOM for dissimilarity data. In P. A. Estévez, J. Príncipe, P. Zegers, & G. Barreto (Eds.), Advances in Self-Organizing Maps (Proceedings of WSOM 2012) (Vol. 198, pp. 13–22). Santiago, Chile: Springer Verlag, Berlin, Heidelberg. Best paper award of the conference

  10. Hernández, N., Biscay, R. J., Villa-Vialaneix, N., & Talavera-Bustamante, I. (2010). A functional density-based nonparametric approach for statistical calibration. In I. Bloch & R. M. Cesar (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 15th Iberoamerican Congress on Pattern Recognition (CIARP 2010) (Vol. 6419, pp. 450–457). Sao Paulo, Brazil: Springer.

  11. Rossi, F., & Villa, N. (2009). Topologically ordered graph clustering via deterministic annealing. In M. Verleysen (Ed.), XVth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2009) (pp. 529–534). Bruges, Belgium: d-side publications.

  12. Rossi, F., & Villa, N. (2008). Consistency of derivative based functional classifiers on sampled data. In M. Verleysen (Ed.), XVIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2008) (pp. 445–450). Bruges, Belgium: d-side publications.

  13. Villa, N., & Rossi, F. (2008). Recent advances in the use of SVM for functional data classification. In S. Dabo-Niang & F. Ferraty (Eds.), Functional and Operatorial Statistics (Prooceedings of First International Workshop on Functional and Operatorial Statistics (IWFOS 2008) (pp. 273–280). Toulouse, France: Physica-Verlag HD.

  14. Villa, N., & Boulet, R. (2007). Clustering a medieval social network by SOM using a kernel based distance measure. In M. Verleysen (Ed.), XVth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2007) (pp. 31–36). Bruges, Belgium: d-side publications.

  15. Villa, N., & Rossi, F. (2007). A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph. In 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefield, Germany: Neuroinformatics Group, Bielefield University. Best paper award of the conference

  16. Rossi, F., & Villa, N. (2005). Classification in Hilbert spaces with support vector machines. In J. Janssen & P. Lenca (Eds.), XIth International Symposium on Applied Stochastic Models and Data Analysis (ASMDA 2005) (pp. 635–642). Brest, France.

  17. Villa, N., & Rossi, F. (2005). Support vector machine for functional data classification. In M. Verleysen (Ed.), XIIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2005) (pp. 467–472). Bruges, Belgium: d-side publications.

Journal editorials

  1. Dang, S., & Vialaneix, N. (2018). Cutting edge bioinformatics and biostatistics approaches are bringing precision medicine to a new era. Lifestyle Genomics, 11(2), 73–76.

  2. Cottrell, M., Olteanu, M., Rouchier, J., & Villa-Vialaneix, N. (2012). Éditorial du numéro spécial RNTI - MASHS 2011/2012 : Modèles et Apprentissage en Sciences Humaines et Sociales. Revue Des Nouvelles Technologies De l’Information, SHS-1, 97–110. In French

  3. Villa-Vialaneix, N., Liaubet, L., & San Cristobal, M. (2011). What is a (good) gene network? Journal of Animal Breeding and Genetics, 128(1), 1–2.

Book chapters

                             

  1. Neuvial, P., Foissac, S., & Vialaneix, N. (2023). Comprendre l’organisation spatiale de l’ADN à l’aide de la statistique. In M. Knoop, S. Blanc, & M. Bouzeghoub (Eds.), L’Interdisciplinarité. Voyage au-del‘a des Disciplines (pp. 172–176). CNRS. In French

  2. Mariette, J., & Vialaneix, N. (2022). Des noyaux pour les omiques. In C. Froidevaux, M.-L. Martin-Magniette, & G. Rigaill (Eds.), Intégration de Données Biologiques (pp. 165–210). iSTE Group. In French

  3. Mariette, J., Olteanu, M., & Vialaneix, N. (2021). Kernel and dissimilarity methods for exploratory analysis in a social context. In A. Daouia & A. Ruiz-Gazen (Eds.), Advances in Contemporary Statistics and Econometrics. Festschrift in Honor of Christine Thomas-Agnan (pp. 669–690). Springer, Cham.

  4. Laguerre, S., Gonzáles, I., Nouaille, S., Moisan, A., Villa-Vialaneix, N., Gaspin, C., Bouvier, M., Carpousis, A. J., Cocaign-Bousquet, M., & Girbal, L. H. D. S. A. M. M. G. (2018). Large-scale measurement of mRNA degradation in Escherichia coli: to delay or not to delay. In A. J. Carpousis (Ed.), High-Density Sequencing Applications in Microbial Molecular Genetics (Vol. 612, pp. 47–66). Cambridge, MA, USA: Elsevier.

  5. Villa-Vialaneix, N., & Canu, S. (2018). Apprentissage connexionniste. In M. Maumy-Bertrand, G. Saporta, & C. Thomas-Agnan (Eds.), Apprentissage Statistique et Données Massives. Paris, France: Éditions TECHNIP. In French

  6. Villa-Vialaneix, N., & Rossi, F. (2018). Méthodes pour l’apprentissage de données massives. In M. Maumy-Bertrand, G. Saporta, & C. Thomas-Agnan (Eds.), Apprentissage Statistique et Données Massives. Paris, France: Éditions TECHNIP. In French

  7. Villa-Vialaneix, N., Liaubet, L., & SanCristobal, M. (2016). Depicting gene co-expression networks underlying eQTLs. In H. N. Kadarmideen (Ed.), Systems Biology in Animal Production and Health (Vol. 2, pp. 1–31). Switzerland: Springer International Publishing. Supplemental material at this link

  8. Follador, M., Villa, N., Paëgelow, M., Renno, F., & Bruno, R. (2008). Tropical deforestation modelling: a comparative analysis of different predictive approaches. The case study of Peten, Guatemala. In M. Paëgelow & M. T. Camacho-Olmedo (Eds.), Modelling Environmental Dynamics (pp. 77–108). Berlin/Heidelberg: Springer.

  9. Paëgelow, M., Camacho-Olmedo, M. T., Ferraty, F., Ferré, L., Sarda, P., & Villa, N. (2008). Prospective modelling of environmental dynamics. A methodological comparison applied to mountain land cover changes. In M. Paëgelow & M. T. Camacho-Olmedo (Eds.), Modelling Environmental Dynamics (pp. 141–168). Berlin/Heidelberg: Springer.

Invitations to conferences

  1. Vialaneix, N. (2024). Réseaux de gènes : inférence, évaluation, utilisation et au-delà. In 55e Journées de Statistique de la SFdS (JdS 2024). Bordeaux, France, Invited speaker.

  2. Vialaneix, N. (2023). Multi-omics data integration methods: kernel and other machine learning approaches. In Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM 2023). Nice, France, Mini-symposium on Multi-omics integration: challenges and perspectives. Invited speaker.

  3. Vialaneix, N. (2022). Multi-omics data integration methods: kernel and other machine learning approaches. In Machine Learning for Life Sciences. Montpellier, France, Invited speaker.

  4. Villa-Vialaneix, N. (2018). Learning from (dis)similarity data. In European R Users Meeting (eRum 2018). Budapest, Hungary, Keynote speaker.

  5. Villa-Vialaneix, N. (2017). Stochastic self-organizing map variants with the R package SOMbrero. In J. C. Lamirel, M. Cottrell, & M. Olteanu (Eds.), 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (Proceedings of WSOM 2017). Nancy, France: IEEE, Keynote speaker.

  6. Villa-Vialaneix, N., Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2016). Random forest for big data. In jstar2016 : Journées de Statistique de Rennes. Rennes, France.

  7. Picheny, V., Servien, R., & Villa-Vialaneix, N. (2016). Interpretable sparse sliced inverse regression for functional data. In Workshop “Learning with Functional Data.” Lille, France.

  8. Olteanu, M., & Villa-Vialaneix, N. (2016). Using SOMbrero for clustering and visualizing complex data. In 9th International Conference of the ERCIM WG on Computational and Methodological Statistics. Seville, Spain.

  9. Cottrell, M., Olteanu, M., Rossi, F., & Villa-Vialaneix, N. (2016). Theoretical and applied aspects of the self-organizing maps. In E. Merényi, M. J. Mendenhall, & O. D. P. (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2016) (Vol. 428, pp. 3–26). Houston, TX, USA: Springer International Publishing Switzerland.

  10. Servien, R., Picheny, V., & Villa-Vialaneix, N. (2016). Interval sparsity for functional inverse regression. In 22nd International Conference on Computational Statistics (COMPSTAT), Satellite CRoNoS Workshop on Functional Data Analysis. Oviedo, Spain.

  11. Villa-Vialaneix, N. (2015). What is a MOOC? In J. Bischoff, B. de Ketelaere, R. Göb, K. Lurz, I. Ograjensek, A. Pievatolo, & M. Reis (Eds.), ENBIS-15 Conference (pp. Prague, Czech Republic). ENBIS Communications and Multimedia Center, Faculty of Economics, University of Ljubjana, Slovenia.

  12. Villa-Vialaneix, N., Vignes, M., Viguerie, N., & San Cristobal, M. (2014). Inferring networks from multiple samples with consensus LASSO. In ENBIS Spring Meeting. Paris, France.

  13. Villa-Vialaneix, N., & Laurent, T. (2013). Permutation tests for labeled network analysis. In 7th International Conference of the ERCIM WG on Computational and Methodological Statistics (ERCIM 2014). London, UK.

  14. Rossi, F., Villa-Vialaneix, N., & Hautefeuille, F. (2011). Exploration of a large database of French charters with social network methods. In International Medieval Congress (IMC 2011), Session 1607 “Problems and Possibilities of Early Medieval Diplomatic, II: Members and Margins.” Leeds, UK.

  15. Rossi, F., Villa-Vialaneix, N., & Hautefeuille, F. (2011). Exploration of a large database of French notarial acts with social network methods. In Digital Diplomatics 2011. Napoli, Italy.

  16. Villa-Vialaneix, N., & Rossi, F. (2010). Classification and regression based on derivatives: a consistency result. In II Simposio sobre Modelamiento Estadístico. Valparaìso, Chile.

  17. Villa-Vialaneix, N., & Rossi, F. (2010). Visualization of graphs by organized clustering: application to social and biological networks. In Workshop on Challenging problems in Statistical Learning (STATLEARN). Paris, France. The videos of the talk are available here

  18. Villa, N., & Rossi, F. (2009). Méthodes de classification organisée pour la recherche de communautés dans les réseaux sociaux. In 38ièmes Journées de Statistique de la SFdS (JdS 2009), 1/2 Journée Satellite STID. Bordeaux, France. In French

  19. Villa, N., Rossi, F., & Truong, Q. D. (2008). Mining a medieval social network by kernel SOM and related methods. In Modèles et Apprentissage en Sciences Humaines et Sociales (MASHS 2008). Créteil, France. This article has been commented on The Physics arXiv Blog

  20. Rossi, F., & Villa, N. (2007). Discrimination de fonctions par Machines à Vecteurs de Support. In 5èmes Journées de Statistique Fonctionnelle et Opérationnelle (pp. 22–23). Lille, France. In French

Other conferences

  1. Jorge, É., Neuvial, P., Vialaneix, N., & Foissac, S. (2024). Procédure de test hiérarchique pour l’analyse de donnés Hi-C. In 55e Journées de Statistique de la SFdS (JdS 2024). Bordeaux, France. In French

  2. Mercadié, A., Gravier, É., Josse, G., Vialaneix, N., & Brouard, C. (2024). Utilisation de la NMF supervisée intégrative pour l’étude d’altérations de la peau. In 55e Journées de Statistique de la SFdS (JdS 2024). Bordeaux, France. In French

  3. Ko, J. H., Rau, A., & Vialaneix, N. (2024). Analyse de la spécificité des associations génétiques dans les études multi-population. In 55e Journées de Statistique de la SFdS (JdS 2024). Bordeaux, France, Forthcoming. In French

  4. Guilmineau, C., Tremblay-Franco, M., Vialaneix, N., & Servien, R. (2024). Analyse différentielle longitudinale des voies métaboliques. In 55e Journées de Statistique de la SFdS (JdS 2024). Bordeaux, France. In French

  5. Malonga, T., Knudsen, C., Lhuillier, É., Aymard, P., Riant, É., Cabau, C., Vialaneix, N., & Beaumont, M. (2024). La transcriptomique en cellule unique révèle les processus de maturation induits dans chaque type de cellule épithéliale intestinale lors de l’introduction de l’alimentation solide. In 41ème CECED (p. P12). Montpellier, France. In French

  6. Liaubet, L., Lefort, G., Reigner, S., Bailly, J., Guilmineau, C., Marty-Gasset, N., Gress, L., Servien, R., Bonnet, A., Gilbert, H., Vialaneix, N., & Quesnel, H. (2023). Neonatal metabolic profiling in relation with biometric phenotypes in two genetic pig lines divergent for residual feed intake. In 11th International Conference on Pig Reproduction (ICPR 2023). Ghent, Belgium, Poster.

  7. Imbert, A., Marty-Gasset, N., Gress, L., Bonnefont, C., Vialaneix, N., Liaubet, L., & Bonnet, A. (2023). Characterization of the endometrial metabolome in late gestation. In 11th International Conference on Pig Reproduction (ICPR 2023) (p. 0053). Ghent, Belgium, Poster.

  8. Bonnet, A., Maman, S., Gress, L., Suin, A., Legoueix, S., Bravo, C., Billon, Y., Vialaneix, N., Bonnefont, C., & Liaubet, L. (2023). Transcriptome of the feto-maternal interface in relation to piglet maturity: part 2 – sow endometrium. In Conference of the European Federation of Animal Science (EAAP 2023) (p. 37.21). Lyon, France, Poster.

  9. Bonnet, A., Maman, S., Gress, L., Suin, A., Bravo, C., Cardenas, G., Billon, Y., Canario, L., Vialaneix, N., Bonnefont, C., & Liaubet, L. (2023). Transcriptome of the feto-maternal interface in relation to piglet maturity: part 1 – fetal placenta. In Conference of the European Federation of Animal Science (EAAP 2023) (p. 37.20). Lyon, France, Poster.

  10. Imbert, A., Duprat, N., Marty-Gasset, N., Gress, L., Canlet, C., Billon, Y., Vialaneix, N., Bonnefont, C., Bonnet, A., & Liaubet, L. (2023). 1H-NMR metabolomic study of Large White and Meishan pigs in late gestation: part 2 – sow endometrium. In Conference of the European Federation of Animal Science (EAAP 2023) (p. 37.22). Lyon, France, Poster.

  11. Mercadié, A., Gravier, É., Josse, G., Vialaneix, N., & Brouard, C. (2023). Extension de la NMF supervisée pour l’intégration de données omiques. In 54e Journées de Statistique de la SFdS (JdS 2023). Bruxelles, Belgium. In French

  12. Guilmineau, C., Tremblay-Franco, M., Vialaneix, N., & Servien, R. (2023). Modélisation de données métabolomiques longitudinales par voies métaboliques. In 54e Journées de Statistique de la SFdS (JdS 2023). Bruxelles, Belgium. In French

  13. Mercadié, A., Gravier, É., Josse, G., Vialaneix, N., & Brouard, C. (2023). Extension de la NMF supervisée pour l’intégration de données omiques. In Journée bioinfo/biostat. Toulouse, France. In French

  14. Maigné, É., Noirot, C., Henry, J., Adu Kesewaah, Y., Badin, L., Déjean, S., Guilmineau, C., Krebs, A., Mathevet, F., Segalini, A., Thomassin, L., Colongo, D., Gaspin, C., Liaubet, L., & Vialaneix, N. (2023). ASTERICS: A Simple Tool for the ExploRation and Integration of omiCS data. In Journée bioinfo/biostat. Toulouse, France.

  15. Maigné, É., Noirot, C., Mariette, J., Adu Kesewaah, Y., Déjean, S., Guilmineau, C., Henry, J., Krebs, A., Liaubet, L., Mathevet, F., Hyphen-Stat, Gaspin, C., & Vialaneix, N. (2022). ASTERICS: A Tool for the ExploRation and Integration of omiCS data. In Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM 2022). Rennes, France, Demo & Poster.

  16. Tête, A., Arnaud, L. C., Le Mentec, H., Gallais, I., Poupin, N., Tournadre, N., Duarte-Hospital, C., Lippi, Y., Mathevet, F., Lefort, G., Burel, A., Surya, R., Boutet-Robinet, E., Shay, W. J., Vialaneix, N., Bortoli, S., Lagadic-Gossmann, D., & Huc, L. (2022). Characterization of human isogenic epithelial cell lines as a relevant tool to study colon carcinogenesis and interaction between genes and environment. In Proceedings of Society of Toxicology Annual Meeting (SOT 2022) (Vol. 3579, p. P204). San Diego, TX, USA, Poster. Co-last author (among 4)

  17. Servien, R., & Vialaneix, N. (2022). Sélection d’intervalles pour des prédicteurs fonctionnels à partir de forêts aléatoires. In 53èmes Journées de Statistique de la SFdS. Lyon, France. In French

  18. Maigné, É., Noirot, C., Mariette, J., Adu Kesewaah, Y., Déjean, S., Guilmineau, C., Henry, J., Krebs, A., Liaubet, L., Mathevet, F., Hyphen-Stat, Gaspin, C., & Vialaneix, N. (2022). ASTERICS: A Tool for the ExploRation and Integration of omiCS data. In 21st European Conference on Computational Biology (ECCB 2022). Sitges, Barcelona, Spain, Poster.

  19. Joo, R., Vialaneix, N., Bivand, R., Eddelbuettel, D., Meyer, D., Basille, M., Boone, M. E., & Zeilis, A. (2022). CRAN Task Views para Guiar a usuaries de paquetes de R. In LatinR 2022. Online conference, In Spanish.

  20. Bougel, C., Servien, R., Vialaneix, N., Canlet, C., Debrauwer, L., Demuth, I., Norman, K., Vetter, V., Dardevet, D., & Polakof, S. (2022). Identification par une approche de métabolomique des voies métaboliques associées à la fragilité dans une cohorte de personnes âgées. In Journées Francophones de la Nutrition. Toulouse, France, Poster. In French

  21. Maigné, É., Noirot, C., Mariette, J., Adu Kesewaah, Y., Déjean, S., Guilmineau, C., Henry, J., Krebs, A., Liaubet, L., Mathevet, F., Hyphen-Stat, Gaspin, C., & Vialaneix, N. (2022). ASTERICS: A Tool for the ExploRation and Integration of omiCS data. In Journées bioinfo/biostat. Toulouse, France, Poster.

  22. Grima, L., Brouard, C., de Givry, S., Goelzer, A., Maigné, É., & Vialaneix, N. (2022). Leçons apprises de l’évaluation des méthodes d’inférence de réseau de gènes chez Bacillus subtilis. In Journées bioinfo/biostat. Toulouse, France, Poster. In French

  23. Tête, A., Arnaud, L., Le Mentec, H., Gallais, I., Tournadre, N., Duarte-Hospital, C., Lippi, Y., Mathevet, F., Lefort, G., Burel, A., Surya, R., Boutet-Robinet, É., Shay, J. W., Nathalie, V., Bortoli, S., Lagadic-Gossmann, D., & Huc, L. (2021). Characterization of human isogenic epithelial cell lines as a relevant tool to study colon carcinogenesis and interaction between genes and environment. In Congrès Annuel de la Société Fran\caise de Biochimie et Biologie Moléculaire (p. 8). Paris, France, Poster. Co-last author (among 4)

  24. Lefort, G., Liaubet, L., Quesnel, H., Marty-Gasset, N., Canlet, C., Vialaneix, N., & Servien, R. (2020). Automatic and joint metabolite identification and quantification of a set of 1H NMR spectra. In 16th Annual Conference of the Metabolomics Society (Metabolomics 2020) (p. 142). Poster.

  25. Lefort, G., Liaubet, L., Canlet, C., Vialaneix, N., & Servien, R. (2020). ASICS: identification and quantification of metabolites in complex 1H NMR spectra. In J. Griffin & F. Jourdan (Eds.), European RFMF Metabomeeting 2020 (p. EC1). Toulouse, France: RFMF and MPF.

  26. Lefort, G., Vialaneix, N., Quesnel, H., Père, M.-C., Billon, Y., Canario, L., Iannuccelli, N., Canlet, C., Paris, A., Servien, R., & Liaubet, L. (2020). Étude de la maturité des porcelets en fin de gestation en utilisant une approche métabolomique multifluide. In 52èmes Journées de la Recherche Porcine. Paris, France: IFIP, INRAE, Poster. In French

  27. Randriamihamison, N., Chavent, M., Foissac, S., Vialaneix, N., & Neuvial, P. (2020). Classification ascendante hiérarchique sous contrainte de contiguïté pour l’analyse différentielle de données Hi-C. In Journées de Statistique de la SFdS (volume exceptionnel).

  28. Foissac, S., Djebali, S., Vialaneix, N., Zytnicki, M., Rau, A., Lagarrigue, S., Acloque, H., & Giuffra, E. (2019). Multi-level conservation of chromosome conformation across livestock species reveals evolutionary links between genome structure and function. In Conference of the International Society for Animal Genetics (ISAG 2019). Llieda, Spain.

  29. Foissac, S., Djebali, S., Munyard, K., Vialaneix, N., Rau, A., Acloque, H., Lagarrigue, S., & Giuffra, E. (2019). Functional annotation of livestock genomes: chromatin structure and regulation of gene expression. In Journal of Animal Science (Proceedings of the ASAS/ASDS Midwest Joint Meeting) (Vol. 97, pp. 15–16). Omaha, NE, USA: Oxford Univ. Press Inc., Cary, NC, USA.

  30. Lahbib-Mansais, Y., Marti-Marimon, M., Vialaneix, N., Foissac, S., Bouissou-Matet, M., & Liaubet, L. (2019). Organisation nucléaire et expression génique lors du développement chez le porc. In Séminaire du réseau EpiPHASE. Castanet-Tolosan, France. In French

  31. Djebali, S., Foissac, S., Vialaneix, N., Munyard, K., Rau, A., Faraut, T., Lagarrigue, S., Acloque, H., & Giuffra, E. (2019). Chromatin accessibility conservation across four livestock species. In Conference of the International Society for Animal Genetics (ISAG 2019). Llieda, Spain.

  32. Lefort, G., Vialaneix, N., Quesnel, H., Père, M.-C., Iannuccelli, N., Canlet, C., Paris, A., Servien, R., & Liaubet, L. (2019). Study of fetal pig maturity in relation with neonatal survival using a multi-fluids metabolomic approach. In 15th Annual Conference of the Metabolomics Society (Metabolomics 2019). The Hague, The Netherlands, Poster.

  33. Lefort, G., Liaubet, L., Canlet, C., Vialaneix, N., & Servien, R. (2019). ASICS : un package R pour l’identification et la quantification de métabolites dans un spectre RMN 1H. In Journées bioinfo/biostat. Toulouse, France, Poster. In French

  34. Mariette, J., Brouard, C., Flamary, R., & Vialaneix, N. (2019). Unsupervised variable selection for kernel methods in systems biology. In Journées bioinfo/biostat. Toulouse, France.

  35. Randriamihamison, N., Neuvial, P., & Vialaneix, N. (2019). Classification ascendante hiérarchique, contrainte d’ordre : conditions d’applicabilité, interprétabilité des dendrogrammes. In Conférence sur l’Apprentissage Automatique (CAp 2019). Toulouse, France, Poster. In French

  36. Lefort, G., Liaubet, L., Canlet, C., Quesnel, H., Vialaneix, N., & Servien, R. (2019). ASICS: a new R package for identification and quantification of metabolites in complex 1H NMR spectra. In useR! 2019. Toulouse, France, Poster.

  37. Lefort, G., Liaubet, L., Quesnel, H., Canlet, C., Vialaneix, N., & Servien, R. (2019). ASICS: identification and quantification of metabolites in complex 1D 1H NMR spectra. In 15th Annual Conference of the Metabolomics Society (Metabolomics 2019). The Hague, The Netherlands, Poster.

  38. Ambroise, C., Dehman, A., Koskas, M., Neuvial, P., Rigaill, G., & Vialaneix, N. (2019). Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. In Statistical Methods for Post Genomic Data (SMPGD 2019). Barcelona, Spain, Poster.

  39. Randriamihamison, N., Neuvial, P., & Vialaneix, N. (2019). Classification ascendante hiérarchique, contrainte d’ordre : conditions d’applicabilité, interprétabilité des dendrogrammes. In 51èmes Journées de Statistique de la SFdS (JdS 2019). Strasbourg, France. In French

  40. Lefort, G., Liaubet, L., Canlet, C., Vialaneix, N., & Servien, R. (2019). ASICS : identifier et quantifier des métabolites dans un spectre RMN 1H. In 51èmes Journées de Statistique de la SFdS (JdS 2019). Strasbourg, France. In French

  41. Lefort, G., Liaubet, L., Tardivel, P., Canlet, C., Tremblay-Franco, M., Debrauwer, L., Villa-Vialaneix, N., & Servien, R. (2018). Using ASICS to quantify metabolites in 1D ^1H NMR spectra: an application to perinatal survival in pigs. In Workshop bioinfo/biostat. Toulouse, France.

  42. Poggi, J.-M., Genuer, R., Villa-Vialaneix, N., & Tuleau-Malot, C. (2018). Random forests for big data. In Joint Statistical Meeting. Vancouver, BC, Canada.

  43. Mestre, C., Djebali, S., Faraut, T., Vialaneix, N., Rau, A., Cabau, C., Zytnicki, M., Derrien, T., Lagarrigue, S., Giuffra, E., & Foissac, S. (2018). Integrative analyses of chromosome conformation, chromatin accessibility and gene expression in human and livestock genomes. In 17th European Conference on Computational Biology. Athens, Greece, Poster.

  44. Lefort, G., Liaubet, L., Canlet, C., Villa-Vialaneix, N., & Servien, R. (2018). ASICS : un package R pour l’identification et la quantification de métabolites dans un spectre RMN 1H. In 7èmes Rencontres R. Rennes, France. In French

  45. Lefort, G., Liaubet, L., Canlet, C., Vialaneix, N., & Servien, R. (2018). ASICS: a new R package for identification and quantification of metabolites in complex 1D 1H NMR spectra. In 17th European Conference on Computational Biology. Athens, Greece, Poster.

  46. Imbert, A., & Villa-Vialaneix, N. (2018). RNAseqNet : un package pour l’inférence de réseaux à partir de données RNA-seq. In 7èmes Rencontres R. Rennes, France. In French

  47. Imbert, A., Valsesia, A., Armenise, C., Lefebvre, G., Gourraud, P.-A., Viguerie, N., & Villa-Vialaneix, N. (2018). Multiple hot-deck imputation for network inference from RNA sequencing data. In 17th European Conference on Computational Biology. Athens, Greece, Highlight talk.

  48. Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2018). Random forests for big data. In Data Science, Statistics & Visualization. Wien, Austria.

  49. Ambroise, C., Dehman, A., Koskas, M., Neuvial, P., Rigaill, G., & Vialaneix, N. (2018). Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. In Journées bioinfo/biostat. Toulouse, France.

  50. Saby-Chaban, C., Zhang, W., Fournier, R., Servien, R., Villa-Vialaneix, N., Corbière, F., & Chastant-Maillard, S. (2017). Progesterone and betahydroxybutyrate in line measurements for a better description and understanding of Holstein cows fertility in field condition. In Proceedings of 8th European Conference on Precision Livestock Farming (EC-PLF). Nantes, France.

  51. Saby-Chaban, C., Zhang, W., Fournier, R., Servien, R., Villa-Vialaneix, N., Cobière, F., & Chastant-Maillard, S. (2017). Reprise de cyclicité post partum et performances de reproduction chez la vache laitière Prim’Holstein en France. In Proceedings of Journées Nationales des Groupements Techniques Vétérinaires. Reims, France. In French

  52. Poggi, J.-M., Genuer, R., Villa-Vialaneix, N., & Tuleau-Malot, C. (2017). Random forests for big data. In 10th International Conference of the ERCIM WG on Computational and Methodological Statistics. London, UK.

  53. Marti-Marimon, M., Acloque, H., Zytnicki, M., Robelin, D., Djebali, S., Villa-Vialaneix, N., Madsen, O., Lahbib-Mansais, Y., Esquerré, D., Mompart, F., Groenen, M., Yerle-Bouissou, M., & Foissac, S. (2017). Characterization of 3D genomic interactions in fetal pig muscle. In Conference of the International Society for Animal Genetics (ISAG 2017). Dublin, Ireland.

  54. Mariette, J., & Villa-Vialaneix, N. (2017). Unsupervised multiple kernel learning to integrate various metagenomic sources. In Proceedings of 4ème Colloque de Génomique Environnementale. Marseille, France, Poster.

  55. Foissac, S., Djebali, S., Acloque, H., Bardou, P., Blanc, F., Cabau, C., Derrien, T., Drouet, F., Esquerré, D., Fabre, S., Gaspin, C., González, I., Goubil, A., Klopp, C., Laurent, F., Marthey, S., Marti-Marimon, M., Mompart, F., Munyard, K., Muret, K., … Giuffra, E. (2017). Profiling the landscape of transcription, chromatin accessibility and chromosome conformation of cattle, pig, chicken and goat genomes. In Conference of the International Society for Animal Genetics (ISAG 2017) (p. 6). Dublin, Ireland.

  56. Djebali, S., Munyard, K., Villa-Vialaneix, N., Cabau, C., Rau, A., Crisci, E., Derrien, T., Klopp, C., Zytnicki, M., Lagarrigue, S., Acloque, H., Foissac, S., & Giuffra, E. (2017). Integrative and differential analysis of transcriptomes and chromatin accessibility regions reveals regulatory mechanisms involved in pig immune and metabolic functions. In Conference of the International Society for Animal Genetics (ISAG 2017) (p. 42). Dublin, Ireland.

  57. Villa-Vialaneix, N., Bontemps, C., & Dejean, S. (2016). Outils pour chercher de l’information sur R et se former. In Rencontres R 2016. Toulouse, France, Lightning talk. In French

  58. Sautron, V., Terenina, E., Gress, L., Lippi, Y., Billon, Y., Villa-Vialaneix, N., & Mormède, P. (2016). Time course of the response to ACTH in pig: biological and transcriptomic study. In Conference of the International Society for Animal Genetics (ISAG). Salt Lake City, UT, USA, Poster.

  59. Sautron, V., Chavent, M., Viguerie, N., & Villa-Vialaneix, N. (2016). Multiway SIR for biological data integration. In Statistical Methods for Post Genomic Data. Lille, France, Poster.

  60. Sautron, V., Chavent, M., Viguerie, N., & Villa-Vialaneix, N. (2016). Multiway-SIR for longitudinal multi-table data integration. In 22nd International Conference on Computational Statistics (COMPSTAT). Oviedo, Spain.

  61. Picheny, V., Servien, R., & Villa-Vialaneix, N. (2016). Parcimonie par intervalle pour la régression inverse par tranche fonctionnelle. In 48e Journées de Statistique de la SFdS (JdS 2016). Montpellier, France. In French

  62. Paniaga, L., Leip, A., Villa-Vialaneix, N., & de Vries, W. (2016). Estimating nitrous oxide fluxes from agricultural soils at European scale using a crop generic meta-model. In 19th Nitrogen Workshop. Skara, Sweden, Poster.

  63. Mariette, J., Chiapello, H., & Villa-Vialaneix, N. (2016). Integrating Tara oceans data sets using multiple kernels. In 15th European Conference on Computational Biology (ECCB 2016), Workshop “Recent Computational Advances in Metagenomics (RCAM).” The Hague, The Netherlands.

  64. Lahbib Mansais, Y., Marti-Marimon, M., Voillet, V., Barasc, H., Mompart, F., Riquet, J., Foissac, S., Robelin, D., Acloque, H., Billon, Y., Villa-Vialaneix, N., Liaubet, L., & Bouissou-Matet, M. (2016). 3D nuclear positioning of IGF2 alleles and trans interactions with imprinted genes. In Conference of the International Society for Animal Genetics (ISAG 2016). Salt Lake City, UT, USA, Poster.

  65. Lahbib-Mansais, Y., Marti Marimon, M., Voillet, V., Barasc, H., Mompart, F., Riquet, J., Foissac, S., Robelin, D., Acloque, H., Billon, Y., Villa-Vialaneix, N., Liaubet, L., & Yerle-Bouissou, M. (2016). 3D nuclear positioning of IGF2 alleles and trans-interactions with imprinted genes in fetal pig cells. In Conference on Genome Architecture in Space and Time. Trieste, Italy, Poster.

  66. Lahbib-Mansais, Y., Marti Marimon, M., Voillet, V., Barasc, H., Mompart, F., Riquet, J., Foissac, S., Robelin, D., Acloque, H., Billon, Y., Villa-Vialaneix, N., Liaubet, L., & Yerle-Bouissou, M. (2016). 3D nuclear positioning of IGF2 alleles and trans interactions with imprinted genes in pig fetal cells. In M. Yerle-Bouissou & A. Pinton (Eds.), Chromosome Research (Proceedings of International Colloquim on Animal Cytogenetics and Genomics, ICACG) (Vol. 24, p. 89). Toulouse, France.

  67. Imbert, A., & Villa-Vialaneix, N. (2016). Outils pour l’analyse et la simulation de données RNA-seq. In Rencontres R 2016. Toulouse, France, Lightning talk. In French

  68. Imbert, A., Le Gall, C., Armenise, C., Lefebvre, G., Hager, J., Valsesia, A., Gourraud, P.-A., Viguerie, N., & Villa-Vialaneix, N. (2016). Imputation de données manquantes pour l’inférence de réseau à partir de données RNA-seq. In 48e Journées de Statistique de la SFdS (JdS 2016). Montpellier, France. In French

  69. Besse, P., Villa-Vialaneix, N., & Ruiz-Gazen, A. (2015). Enseigner la statistique pour l’analyse de mégadonnées. In 47e Journées de Statistique de la SFdS (JdS 2015). Lille, France. In French

  70. Villa-Vialaneix, N., & Olteanu, M. (2015). Multiple dissimilarity SOM for clustering and visualizing graphs with node and edge attributes. In International Conference on Machine Learning (ICML 2015), Workshop FEAST. Poster.

  71. Olteanu, M., & Villa-Vialaneix, N. (2015). Classification et visualisation de graphes avec SOMbrero. In 4èmes Rencontres R. Grenoble, France. In French

  72. Genuer, R., Poggi, J.-M., Tuleau, C., & Villa-Vialaneix, N. (2015). Random forests and big data. In 47e Journées de Statistique de la SFdS (JdS 2015). Lille, France.

  73. Villa-Vialaneix, N. (2014). J’ai testé pour vous... un MOOC. In 46e Journées de Statistique de la SFdS (JdS 2014). Rennes, France. In French

  74. Sautron, V., Terenina, E., Mormède, P., & Villa-Vialaneix, N. (2014). Genetics systems of stress responses in pigs. In Bioinformatics/Biostatistics regional workshop. Toulouse, France.

  75. Sautron, V., Terenina, E., Merlot, E., Martin, P., Lippi, Y., Liaubet, L., Prunier, A., Mormède, P., & Villa-Vialaneix, N. (2014). Longitudinal CCA to analyze stress responses in pigs. In European Conference on Computational Biology (ECCB 2014). Strasbourg, France, Poster.

  76. Picheny, V., Vandel, J., Vignes, M., & Villa-Vialaneix, N. (2014). Reconstruction quality of a biological network when its constituting elements are partially observed. In AI & Statistics. Reykjavik, Iceland.

  77. Olteanu, M., & Villa-Vialaneix, N. (2014). Self-organizing maps for clustering visualization of bipartite graphs. In 46e Journées de Statistique de la SFdS (JdS 2014). Rennes, France.

  78. Montastier, E., Villa-Vialaneix, N., Gonzalez, I., Caspar-Bauguil, S., Saris, W. H. M., Langin, D., Kunesova, M., & Viguerie, N. (2014). Adipose tissue signatures related to weight changes in response to calorie restriction and subsequent weight maintenance using lipidome and gene profiling network analysis. In Bioinformatics/Biostatistics regional workshop. Toulouse, France.

  79. Merlot, E., Prunier, A., Damon, M., Vignoles, F., Villa-Vialaneix, N., Morède, P., & Terenina, E. (2014). Blood transcriptome response to LPS in pigs. In Proceedings of Annual Meeting of the European Federation of Animal Science (EAAP) (Vol. 20). Copenhagen, DK: Wageningen Academic Publischer, Wageningen (The Netherlands).

  80. Hernández, N., Biscay, R. J., Villa-Vialaneix, N., & Talavera, I. (2014). Density-based inverse calibration with functional predictors. In 11th International Conference on Operations Research (ICOR 2014). Havana, Cuba.

  81. Villa-Vialaneix, N., & San Cristobal, M. (2013). Consensus LASSO : inférence conjointe de réseaux de gènes dans des conditions expérimentales multiples. In 45e Journées de Statistique de la SFdS (JdS 2013). Toulouse, France. In French

  82. Villa-Vialaneix, N., Olteanu, M., & Cierco-Ayrolles, C. (2013). Carte auto-organisatrice pour graphes étiquetés. In Colloque Extraction et Gestion de Connaissances (EGC 2013), ateliers Fouille de Grands Graphes (FGG). Toulouse, France. In French

  83. Leroux, D., & Villa-Vialaneix, N. (2013). sexy-rgtk: a package for programming RGtk2 GUI in a user-friendly manner. In 2èmes Rencontres R BoRdeaux. Lyon, France. Slides on slideshare

  84. Brunet, F., Mariette, J., Cierco-Ayrolles, C., Gaspin, C., Bardou, P., & Villa-Vialaneix, N. (2013). Classification d’un graphe de co-expression avec des méta-données pour la détection de micro-RNAs. In Modèles et l’Analyse des Réseaux : Approches Mathématiques et Informatiques (MARAMI 2013). Saint-Étienne, France. In French

  85. Bendhaïba, L., Olteanu, M., & Villa-Vialaneix, N. (2013). SOMbrero : cartes auto-organisatrices stochastiques pour l’intégration de données décrites par des tableaux de dissimilarités. In 2èmes Rencontres R BoRdeaux. Lyon, France. Slides on slideshare In French

  86. Villa-Vialaneix, N., Rossi, F., & Hautefeuille, F. (2012). Spatial correlation in bipartite networks: the impact of the geographical distances on the relations in a corpus of medieval transactions. In Modèles et Apprentissage en Sciences Humaines et Sociales (MASHS 2012). Paris, France.

  87. Villa-Vialaneix, N., Rossi, F., & Hautefeuille, F. (2012). Exploration relationnelle d’un corpus d’actes notariés médiévaux. In Colloque Configuration(s). Paris, France. In French

  88. Villa-Vialaneix, N., Edwards, N. A., Liaubet, L., & Viguerie, N. (2012). Comparison of network inference packages and methods for multiple network inference. In 1ères Rencontres R BoRdeaux. BoRdeaux, France.

  89. San Cristobal, M., Boitard, S., Fariello Rico, M. I., Gilbert, H., Liaubet, L., Paris, A., Rogel Gaillard, C., Rohart, F., Riquet, J., Servin, B., Villa-Vialaneix, N., Sanchez, M. P., & Milan, D. (2012). Genetic and phenotypic fine characterizations of French porcine reference populations. In Conference of the International Society for Animal Genetics (ISAG). Cairns, Australia, Poster.

  90. Laurent, T., & Villa-Vialaneix, N. (2012). Analyse de données pour des graphes étiquetés. In 44èmes Journées de Statistique de la SFdS (JdS 2012). Bruxelles, Belgique. In French

  91. Villa-Vialaneix, N., Liaubet, L., Laurent, T., Gamot, A., Cherel, P., & San Cristobal, M. (2011). L’analyse d’un réseau de co-expression génique met en valeur des groupes fonctionnels homogènes et des gènes importants relatifs a un phénotype d’intérêt. In Actes des 43èmes Journées de Statistique, Société Française de Statistique. Tunis, Tunisie. In French

  92. San Cristobal, M., Boitard, S., Bouffaud, M., Canlet, C., Chaltiel, L., Chevalet, C., Dehais, P., Dumont, M., Fariello Rico, M. I., Gilbert, H., Gut, I., Iannuccelli, N., Klopp, C., Laurent, B., Li, Z., Liaubet, L., Mercat-Gernigon, M. J., Milan, D., Molina, J., Muller, N., … Villa-Vialaneix, N. (2011). Diversité et biologie intégrative : des pistes à explorer pour combler le gap entre diversité génétique et diversité phénotypique. In Colloque FRB : les Ressources Génétiques (RG) face aux nouveaux enjeux environnementaux, économiques et sociétaux. Montpellier, France, Poster. In French

  93. Leip, A., Follador, M., Tarantola, S., Busto, M., & Villa-Vialaneix, N. (2011). Sensitivity of the process-based model DNDC on microbiological parameters. In Nitrogen and Global Change - Key findings & Future challenges. Edinburgh, UK.

  94. Villa-Vialaneix, N., Follador, M., & Leip, A. (2010). A comparison of three learning methods to predict N2O fluxes and N leaching. In C. Bienacki, E. Masson, A. Lendasse, & E. Séverin (Eds.), Modèles et Apprentissage en Sciences Humaines et Sociales (MASHS 2010) (pp. 57–64). Lille, France: Multiprint Oy (Espoo, Finland).

  95. Rohart, F., Villa-Vialaneix, N., Paris, A., Molina, J., Canlet, C., Milan, D., Laurent, B., & SanCristobal, M. (2010). Phenotypic prediction based on metabolomic data: LASSO vs BOLASSO, primary data vs wavelet data. In Gesellschaft für Tierzuchtwissenschaften e. V. (Ed.), World Congress on Genetics Applied to Livestock Production (WCGALP 2010). Leipzig, Germany.

  96. Liaubet, L., Villa-Vialaneix, N., Gamot, A., Rossi, F., Chérel, P., & SanCristobal, M. (2010). The structure of a gene network reveals 7 biological functions underlying eQTLs in pig. In Gesellschaft für Tierzuchtwissenschaften e. V. (Ed.), World Congress on Genetics Applied to Livestock Production (WCGALP 2010). Leipzig, Germany.

  97. Laurent, T., & Villa-Vialaneix, N. (2010). Analysis of the influence of a network on the values of its nodes: the use of spatial indexes. In 1ère Conférence Modèles et Analyse des Réseaux : Approches Mathématiques et Informatique (MARAMI 2010). Toulouse, France.

  98. Villa, N., Dkaki, T., Gadat, S., Inglebert, J. M., & Truong, Q. D. (2009). Recherche et représentation de communautés dans des grands graphes. In 2ème Séminaire Veille Stratégique, Scientifique et Technologique (VSST 2009). Nancy, France. In French

  99. Gamot, A., Villa, N., Liaubet, L., Rossi, F., Tosser-Klopp, G., Chérel, P., & San Cristobal, M. (2009). Are gene networks always meaningful? In European Animal Disease Genomics Network of Excellence for Animal Health and Food Safety (EADGENE Days). Paris, France.

  100. Follador, M., Renno, F., Bruno, R., Paëgelow, M., Villa, N., & Mas, J. F. (2007). Remote sensing, GIS and predictive methods: a new approach to environmental and hazard problems. In Sesto Forum Italiano di Scienze della Terra (GeoItalia 2007). Rimini, Italy.

  101. Boulet, R., Hautefeuille, F., Jouve, B., Kuntz, P., Le Goffic, B., Picarougne, F., & Villa, N. (2007). Sur l’analyse de réseaux de sociabilité dans la société paysanne médiévale. In Modèles et Apprentissage en Sciences Humaines et Sociales (MASHS 2007). Brest, France. In French

  102. Villa, N., & Rossi, F. (2006). SVM fonctionnels par interpolation spline. In 38ièmes Journées de Statistique de la SFdS (JdS 2006). Clamart, France. In French

  103. Bruno, R., Follador, M., Paëgelow, M., Renno, F., & Villa, N. (2006). Integrating remote sensing, GIS and prediction models to monitor the deforestation and erosion in Peten reserve, Guatemala. In E. Pirard, A. Dassargues, & H. S. Havenith (Eds.), XIth International Congress for Mathematical Geology (IAMG 2006). Liège, Belgium.

Theses

  1. Villa-Vialaneix, N. (2014, November). Contributions à l’analyse de données non vectorielles (Habilitation à Diriger des Recherches de l’université Toulouse 1 (Capitole), soutenue le 13 novembre 2014). Université Toulouse 1 (Capitole), Toulouse, France. In French

  2. Villa-Vialaneix, N. (2005, October). Éléments d’Apprentissage en Statistique Fonctionnelle. Classification et Régression Fonctionnelles par Réseaux de Neurones et Support Vector Machine (Thèse de doctorat de l’Université Toulouse 2 (Le Mirail), soutenue le 21 octobre 2005). Université Toulouse II (Le Mirail), Toulouse, France.

Other

  1. Chastant-Maillard, S., Saby-Chaban, C., Zhang, W., Fournier, R., Servien, R., & Villa-Vialaneix, N. (2019). Reprise atypique de la cyclicité ovarienne chez la vache laitière : une forte association avec la cétose. Le Point Vétérinaire.fr. In French

  2. Villa-Vialaneix, N. (2015). Note de consultation : “Analyse des données multidimensionnelles” (MOOC, F. Husson et al., 2015). Statistique Et Enseignement, 6(2), 61–66. In French

  3. San Cristobal, M., Sanchez, M. P., Mercat, M. J., Rohart, F., Liaubet, L., Tribout, T., Canlet, C., Muller, N., Molina, J., Iannuccelli, N., Laurent, B., Villa-Vialaneix, N., Paris, A., & Milan, D. (2014). Le métabolome, un moyen pour trouver de nouveaux biomarqueurs ? Viandes Et Produits Carnés, (VPC-2014-30-2-1). In French

  4. Villa-Vialaneix, N. (2014). Prédire les rejets d’azote agricole pour mieux les contrôler. In M. Andler, L. Bel, S. Benzoni, T. Goudon, C. Imbert, & A. Rousseau (Eds.), Brèves de Maths. Éditions Nouveau Monde. In French

  5. Villa-Vialaneix, N. (2013). Note de lecture : “Régression avec R” (P.A. Cornillon et E. Matzner-Løber, 2011). Statistique Et Enseignement, 4(2), 87–89. In French

  6. Villa-Vialaneix, N. (2012). Note de lecture : “Méthodes de Monte-Carlo avec R” (Chr. P. Robert et G. Casella, 2011). Statistique Et Enseignement, 3(1), 113–114. In French



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