Publications

Journal articles

  1. 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. Forthcoming.

  2. 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.

  3. Mariette, J., & Villa-Vialaneix, N. (2017). Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics. Forthcoming.

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

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

  6. Dou, S., Villa-Vialaneix, N., Liaubet, L., Billon, Y., Giorgi, M., and Gilbert, H., Gourdine, J. L., Riquet, J., & Renaudeau, D. (2017). ^\textrm1HNMR-based metabolomic profiling method to develop plasma biomarkers for sensitivity to chronic heat stress in growing pigs. PLoS ONE. Forthcoming.

  7. 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.

  8. 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.

  9. 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.

  10. 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. First co-author.

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

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

  13. 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

  14. 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.

  15. 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.

  16. 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.

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

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

  19. 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.

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

  21. 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.

  22. 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

  23. 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.

  24. 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.

  25. 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

  26. 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.

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

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

  29. 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

  30. 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.

  31. 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

  32. 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.

  33. 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.

  34. Stage, A. R., & Crookston, N. L. (2007). Partitioning error components for accuracy-assessment of near-neighbor methods of imputation. Forest Science, 53(1), 62–72.

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

  36. 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

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

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

  39. 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. 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.

  3. 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.

  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. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  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., & 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

  15. 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.

  16. 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.

  17. 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.

Journal editorials

  1. 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.

  2. 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. Villa-Vialaneix, N., & Rossi, F. (2017). Actes des Journées d’Études en Statistique, Apprentissage. In G. Saporta, M. Maumy-Bertrand, & C. Thomas-Agnan (Eds.), . Economica.

  2. Villa-Vialaneix, N., & Canu, S. (2017). Actes des Journées d’Études en Statistique, Apprentissage. In G. Saporta, M. Maumy-Bertrand, & C. Thomas-Agnan (Eds.), . Economica, Forthcoming.

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

  4. Paëgelow, M., Camacho-Olmedo, M. T., Ferraty, F., Ferré, L., Sarda, P., & Villa, N. (2008). Modelling Environmental Dynamics. In M. Paëgelow & M. T. Camacho-Olmedo (Eds.), (pp. 141–168). Berlin/Heidelberg: Springer.

  5. Follador, M., Villa, N., Paëgelow, M., Renno, F., & Bruno, R. (2008). Modelling Environmental Dynamics. In M. Paëgelow & M. T. Camacho-Olmedo (Eds.), (pp. 77–108). Berlin/Heidelberg: Springer.

Invitations to conferences

  1. 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.

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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

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

  13. 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

  14. 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

  15. 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

  16. 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. 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.

  2. 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.

  3. 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.

  4. Foissac, S., Djebali, S., Acloque, H., Bardou, P., Blanc, F., Cabau, C., Derrien, T., Drouet, F., Esquerré, D., Fabre, S., Gaspin, C., Gonzalez, 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.

  5. 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.

  6. 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.

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

  8. 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.

  9. 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.

  10. Mariette, J., Chiapello, H., & Villa-Vialaneix, N. (2016). Integrating \emphTara 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

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

  17. 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.

  18. 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 \it 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.

  19. 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 \it trans-interactions with imprinted genes in fetal pig cells. In Conference on Genome Architecture in Space and Time. Trieste, Italy, Poster.

  20. 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.

  21. 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.

  22. 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

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

  24. 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.

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

  26. 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.

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

  28. 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.

  29. 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).

  30. 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.

  31. 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.

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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.

  38. 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

  39. 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

  40. 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.

  41. 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.

  42. 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.

  43. 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 gént́ique et diversité phénotypique. In Colloque FRB : les Ressources Génétiques (RG) face aux nouveaux enjeux environnementaux, économiques et sociétaux. Montpellier, France, Poster.

  44. 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

  45. 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).

  46. 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.

  47. 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.

  48. 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.

  49. 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.

  50. 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

  51. 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.

  52. 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

  53. 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.

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

Theses

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

  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. 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

  2. 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

  3. Villa-Vialaneix, N. (2014). Brèves de Maths. In M. Andler, L. Bel, S. Benzoni, T. Goudon, C. Imbert, & A. Rousseau (Eds.), . Éditions Nouveau Monde.

  4. 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

  5. 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