A. Abbott and J. Forrest, Optimal Matching Methods for Historical Sequences, Journal of Interdisciplinary History, vol.16, issue.3, pp.471-494, 1986.
DOI : 10.2307/204500

L. A. Adamic and N. Glance, The political blogosphere and the 2004 US election : divided they blog, Proceedings of the 3rd LINKDD Workshop, pp.36-43, 2005.
DOI : 10.1145/1134271.1134277

J. Aitchison, The statistical analysis of compositional data, Journal of the Royal Statistical Society, Series B, vol.442, issue.100, pp.139-177, 1982.
DOI : 10.1007/978-94-009-4109-0

U. David, A. Oren, L. Jessica, and K. , A new look at the statistical model identification, In : Cell, vol.1436, pp.1005-1022, 2010.

C. Ambroise and G. Govaert, Analyzing dissimilarity matrices via Kohonen maps, Proceedings of 5th Conference of the International Federation of Classification Societies (IFCS 1996). T. 2. Kobe (Japan), pp.96-99, 1996.

P. Andras, KERNEL-KOHONEN NETWORKS, International Journal of Neural Systems, vol.6, issue.6, pp.117-135, 2002.
DOI : 10.1016/S0893-6080(99)00041-6

N. Aronszajn, Theory of reproducing kernels, In : Transactions of the American Mathematical Society, vol.683, issue.101, pp.337-404, 1950.

A. Manimozhiyan, R. Jeroen, and P. Eric, Enterotypes of the human gut microbiome, Nature, vol.473, pp.174-180, 2011.

B. Philippe, M. Jérôme, E. Frédéric, D. Christophe, and K. Christophe, jvenn : an interactive Venn diagram viewer, BMC bioinformatics, vol.151, pp.293-107, 2014.

B. Baruque and E. Corchado, Fusion methods for unsupervised learning ensembles. T. 322. Studies in Computational Intelligence, pp.48-52, 2011.
DOI : 10.1007/978-3-642-16205-3

A. Ben-hur and J. Weston, Data Mining Techniques for the Life Sciences. T. 609. Methods in Molecular Biology, pp.223-239, 2010.

H. Wolfgang, F. L. Berger, and . Parker, Diversity of Planktonic Foraminifera in Deep-Sea Sediments, In : Science, vol.3937168, pp.1345-1347, 1970.

B. Matteo, M. Ettore, and R. Daniel, Methods for the integration of multi-omics data : mathematical aspects, BMC Bioinformatics, vol.1517, p.36, 2016.

P. Bickel, F. G¨otzeg¨otze, W. Van, and Z. , Resampling Fewer Than n Observations: Gains, Losses, and Remedies for Losses, In : Statistica Sinica, vol.17, pp.1-31, 1997.
DOI : 10.1007/978-1-4614-1314-1_17

E. De, B. , M. Cottrell, and M. Verleisen, Statistical tools to assess the reliability of selforganizing maps, Neural Networks, vol.158, issue.9, pp.967-978, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00122768

J. Boelaert, L. Bendha¨ibabendha¨iba, M. Olteanu, N. Villa-vialaneix, F. M. Villmann et al., SOMbrero : an R package for numeric and non-numeric self-organizing maps Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014), Advances in Intelligent Systems and Computing. Mittweida, pp.219-228, 2014.

P. Bork, C. Bowler, C. De, and . Vargas, Tara Oceans studies plankton at planetary scale, Science, vol.15, issue.10, pp.873-873, 2015.
DOI : 10.1371/journal.pbio.1001177

URL : https://hal.archives-ouvertes.fr/hal-01258211

R. Boulet, B. Jouve, F. Rossi, and N. Villa, Batch kernel SOM and related Laplacian methods for social network analysis, Neurocomputing, vol.71, issue.7-9, pp.1257-1273, 2008.
DOI : 10.1016/j.neucom.2007.12.026

URL : https://hal.archives-ouvertes.fr/hal-00202339

C. Bouveyron and C. Brunet-saumard, Model-based clustering of high-dimensional data: A review, Computational Statistics & Data Analysis, vol.71, pp.52-78, 2014.
DOI : 10.1016/j.csda.2012.12.008

URL : https://hal.archives-ouvertes.fr/hal-00750909

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed optimization and statistical learning via the alterning direction method of multipliers, Foundations and Trends in Machine Learning, vol.31, pp.1-122, 2011.

R. J. Bray and J. T. Curtis, An Ordination of the Upland Forest Communities of Southern Wisconsin, Ecological Monographs, vol.27, issue.4, pp.325-349, 1957.
DOI : 10.2307/1942268

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

J. R. Brum, J. C. Ignacio-espinoza, and S. Roux, Patterns and ecological drivers of ocean viral communities, Science, vol.73, issue.4, 2015.
DOI : 10.1111/j.1467-9868.2010.00749.x

URL : https://hal.archives-ouvertes.fr/hal-01253982

M. B?adoiub?adoiu, S. Har-peled, and P. Indyk, Approximate clustering via core-sets, Proceedings of the 34th annual ACM Symposium on Theory of Computing, pp.250-257

J. G. Caporaso, J. Kuczynski, and J. Stombaugh, QIIME allows analysis of high-throughput community sequencing data, Nature Methods, vol.8, issue.5, pp.335-336, 2010.
DOI : 10.1038/nmeth.f.303

A. Chao and T. J. Shen, Nonparametric estimation of Shannon's index of diversity when there are unseen species in sample, Environmental and Ecological Statistics, vol.410, pp.429-443, 2003.

J. Chen, H. L. Li, Y. Hu, J. Liu, and . Lin, Kernel Methods for Regression Analysis of Microbiome Compositional Data, Topics in Applied Statistics Proceedings in Mathematics & Statistics (PROMS), pp.191-201, 2013.
DOI : 10.1007/978-1-4614-7846-1_16

J. Chen, K. Bittinger, and E. Charlson, Associating microbiome composition with environmental covariates using generalized UniFrac distances, Bioinformatics, vol.28, issue.16, pp.2106-2113, 2012.
DOI : 10.1093/bioinformatics/bts342

URL : https://academic.oup.com/bioinformatics/article-pdf/28/16/2106/16905186/bts342.pdf

X. Chen and M. G. Xie, A split-and-conquer approach for analysis of extraordinarily large data, Statistica Sinica, vol.24, pp.1655-1684, 2014.

Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi, and L. Cazzanti, Similarity-based classification : concepts and algorithm, Journal of Machine Learning Research, vol.10, pp.747-776, 2009.

C. T. Chu, S. K. Kim, and Y. A. Lin, Map-Reduce for machine learning on multicore (6?11 déc Sous la dir, Advances in Neural Information Processing Systems, pp.281-288, 2010.

J. R. Cole, Q. Wang, and J. A. Fish, Ribosomal Database Project: data and tools for high throughput rRNA analysis, Nucleic Acids Research, vol.42, issue.D1, pp.633-642, 2014.
DOI : 10.1093/nar/gkt1244

URL : https://academic.oup.com/nar/article-pdf/42/D1/D633/16952330/gkt1244.pdf

E. C?omec?ome, M. Cottrell, and P. Gaubert, Analysis of professional trajectories using disconnected self-organizing maps Advances in Self-Organizing Maps Subtitle of the special issue : Selected Papers from the Workshop on Self-Organizing Maps, Neurocomputing, vol.147, pp.185-196, 2012.

B. Conan-guez, F. Rossi, A. El, and . Golli, Fast algorithm and implementation of dissimilarity self-organizing maps, Neural Networks, vol.19, issue.6-7, pp.6-7, 2006.
DOI : 10.1016/j.neunet.2006.05.002

URL : https://hal.archives-ouvertes.fr/inria-00174196

B. Conan-guez, F. Rossi, A. El, and . Golli, Fast algorithm and implementation of dissimilarity self-organizing maps, Neural Networks, vol.19, issue.6-7, pp.6-7, 2006.
DOI : 10.1016/j.neunet.2006.05.002

URL : https://hal.archives-ouvertes.fr/inria-00174196

P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, Modeling wine preferences by data mining from physicochemical properties, Decision Support Systems, pp.547-553, 2009.
DOI : 10.1016/j.dss.2009.05.016

M. Cottrell and P. Letrémy, How to use the Kohonen algorithm to simultaneously analyze individuals and modalities in a survey, Neurocomputing, vol.63, issue.88, pp.193-207, 2005.
DOI : 10.1016/j.neucom.2004.04.011

M. Cottrell, E. De, B. , M. Verleisen-allinson, H. Yin et al., A statistical tool to assess the reliability of selforganizing maps Advances in Self-Organizing Maps (Proceedings of WSOM 2001). (13? 15 juin, pp.7-14, 2001.

M. Cottrell, J. C. Fort, and G. Pag-`-pag-`-es, Theoretical aspects of the SOM algorithm, Neurocomputing, vol.21, issue.1-3, pp.119-138, 1998.
DOI : 10.1016/S0925-2312(98)00034-4

URL : https://hal.archives-ouvertes.fr/hal-00141470

L. Diana, . Cox-foster, C. Sean, and E. C. Holmes, A Metagenomic Survey of Microbes in Honey Bee Colony Collapse Disorder, In : Science, vol.3185848, pp.283-287, 2007.

L. J. Crone and D. S. Crosby, Statistical Applications of a Metric on Subspaces to Satellite Meteorology, Technometrics, vol.60, issue.3, pp.324-328, 1995.
DOI : 10.1214/aoms/1177728846

L. Danon, A. Diaz-guilera, J. Duch, and A. Arenas, Comparing community structure identification, Journal of Statistical Mechanics: Theory and Experiment, vol.2005, issue.09, pp.9008-51, 2005.
DOI : 10.1088/1742-5468/2005/09/P09008

M. Denil, D. Matheson, N. De, and F. , Consistency of online random forests, Proceedings of the 30th International Conference on Machine Learning, pp.1256-1264, 2013.

T. Z. Desantis, P. Hugenholtz, and N. Larsen, Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB, Applied and Environmental Microbiology, vol.72, issue.7, pp.5069-5072, 2006.
DOI : 10.1128/AEM.03006-05

M. A. Dillies, A. Rau, and J. Aubert, A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis, Briefings in Bioinformatics, vol.14, issue.6, p.22, 2012.
DOI : 10.1093/bib/bbs046

URL : https://hal.archives-ouvertes.fr/hal-01521274

S. Dray, S. Pavoine, D. Aguirre-de, and C. , Considering external information to improve the phylogenetic comparison of microbial communities: a new approach based on constrained Double Principal Coordinates Analysis (cDPCoA), Molecular Ecology Resources, vol.38, issue.2, pp.242-249, 2014.
DOI : 10.1093/nar/gkq066

P. Drineas, M. Mahoney, and S. Muthukrishnan, Relative-Error $CUR$ Matrix Decompositions, SIAM Journal on Matrix Analysis and Applications, vol.30, issue.2, pp.844-881, 2008.
DOI : 10.1137/07070471X

URL : http://arxiv.org/pdf/0708.3696

E. R. Dusko and C. Metahit, Metagenomics of the intestinal microbiota: potential applications, Gastroent??rologie Clinique et Biologique, vol.34, pp.23-28, 2010.
DOI : 10.1016/S0399-8320(10)70017-8

A. El-golli, B. Conan-guez, and F. Rossi, A Self-Organizing Map for Dissimilarity Data, pp.61-68, 2004.
DOI : 10.1007/978-3-642-17103-1_7

C. H. Elzinga, Sequence Similarity, Sociological Methods & Research, vol.32, issue.1, pp.3-29, 2003.
DOI : 10.1177/0049124100029001003

F. Escudie, L. Auer, and M. Bernard, FROGS : Find Rapidly OTU with Galaxy Solution " . In : In : The environmental genomic Conference, p.24, 2015.

F. Karoline, J. Fah, S. Jacques, and I. , Microbial Co-occurrence Relationships in the Human Microbiome, PLOS Computational Biology, vol.87, pp.1002606-1002630, 2012.

F. Noah, J. W. Leff, and B. J. Adams, Cross-biome metagenomic analyses of soil microbial communities and their functional attributes, Proceedings of the National Academy of Sciences, pp.21390-21395, 2012.

F. Fouss, A. Pirotte, J. M. Renders, and M. Saerens, Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation, IEEE Transactions on Knowledge and Data Engineering, vol.19, issue.3, pp.355-369, 2007.
DOI : 10.1109/TKDE.2007.46

E. A. Franzosa, H. Tiffany, and A. Sirota-madi, Sequencing and beyond: integrating molecular 'omics' for microbial community profiling, Nature Reviews Microbiology, vol.7, issue.6, pp.360-372, 2015.
DOI : 10.1038/ismej.2013.139

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4800835/pdf

T. Fruchterman and B. Reingold, Graph drawing by force-directed placement, Software: Practice and Experience, vol.41, issue.11, pp.1129-1164, 1991.
DOI : 10.1007/978-1-4613-1627-5

URL : http://www.cs.ubc.ca/local/reading/proceedings/spe91-95/spe/./vol21/issue11/spe060tf.pdf

A. Georgakis, H. Li, and M. Gordan, An ensemble of SOM networks for document organization and retrieval AKRR, Proceedings of International Conference on Adaptive Knowledge Representation and Reasoning (AKRR 2005). 2005 (cf, p.49, 2005.

A. Gisbrecht, A. Shultz, and B. Hammer, Parametric nonlinear dimensionality reduction using kernel t-SNE, Neurocomputing, vol.147, pp.71-82, 2015.
DOI : 10.1016/j.neucom.2013.11.045

A. Gisbrecht, B. Mokbel, and B. Hammer, The Nyström approximation for relational generative topographic mappings, English. In : NIPS workshop on challenges of Data Visualization. (11 déc, pp.2010-72, 2010.
DOI : 10.1016/j.neucom.2010.12.011

URL : http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2010-94.pdf

A. Gittens and M. W. Mahoney, Revisiting the Nystrom method for improved large-scale machine learning, Journal of Machine Learning Research, vol.283, pp.567-575, 2013.

L. Goldfarb, A unified approach to pattern recognition, Pattern Recognition, vol.17, issue.5, pp.575-582, 1984.
DOI : 10.1016/0031-3203(84)90056-6

M. G¨oneng¨onen and E. Alpaydin, Multiple kernel learning algorithms, Journal of Machine Learning Research, vol.12, pp.2211-2268, 2011.

S. Goodwin, J. D. Mcpherson, W. Richard, and M. , Coming of age: ten years of next-generation sequencing technologies, Nature Reviews Genetics, vol.4, issue.6, pp.333-351, 2016.
DOI : 10.1111/j.1755-0998.2011.03024.x

T. Graepel, M. Burger, and K. Obermayer, Self-organizing maps: Generalizations and new optimization techniques, Neurocomputing, vol.21, issue.1-3, pp.173-190, 1998.
DOI : 10.1016/S0925-2312(98)00035-6

URL : http://stat.cs.tu-berlin.de/~guru/papers/graepel98_neurocomp_SOM.ps.gz

L. Guidi, S. Chaffron, and L. Bittner, Plankton networks driving carbon export in the oligotrophic ocean, Nature, vol.18, issue.7600, pp.465-470, 2016.
DOI : 10.18637/jss.v018.i02

URL : https://hal.archives-ouvertes.fr/hal-01275276

B. Hammer and A. Hasenfuss, Topographic Mapping of Large Dissimilarity Data Sets, Neural Computation, vol.2005, issue.9, pp.2229-2284, 2010.
DOI : 10.1162/jmlr.2003.4.6.1001

B. Hammer, A. Hasenfuss, F. Rossi, and M. Strickert, Topographic processing of relational data, Proceedings of the 6th Workshop on Self-Organizing Maps (WSOM 07, p.34, 2007.

P. D. Hebert, E. H. Penton, J. M. Burns, D. H. Janzen, and W. Hallwachs, Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator, Proceedings of the National Academy of Sciences, vol.7, issue.2, pp.41-14812, 2004.
DOI : 10.1006/mpev.1996.0388

T. Heskes-'e, S. Oja, . Kaski, and . Amsterdam, Energy functions for self-organizing maps In : Kohonen Maps, pp.303-315, 1999.

R. R. Hochking, The analysis and selection of variables in linear regression, Biometrics, p.71, 1976.

D. Hofmann and B. Hammer, Sparse approximations for kernel learning vector quantization Sous la dir, pp.549-554, 2013.

]. D. Bibliographie73, A. Hofmann, B. Gisbrecht, and . Hammer, Efficient approximations of robust soft learning vector quantization for non-vectorial data, Neurocomputing, vol.147, pp.96-106, 2015.

D. Hofmann, F. M. Schleif, B. Paass, B. En, and . Hammer, Learning interpretable kernelized prototype-based models, Neurocomputing, vol.141, pp.84-96, 2014.
DOI : 10.1016/j.neucom.2014.03.003

E. R. Holzinger, S. M. Dudek, A. T. Frase, S. A. Pendergrass, and M. D. Ritchie, Transitivity in structural models of small groups, In : Bioinformatics, vol.305, pp.698-705, 2014.

L. A. Hug, B. J. Baker, and A. Karthik, A new view of the tree of life, Nature Microbiology, vol.1, issue.5, p.18, 16048.
DOI : 10.1128/mBio.00708-13

C. Huttenhower, D. Gevers, and R. Knight, Structure, function and diversity of the healthy human microbiome, Nature, vol.25, issue.7402, pp.207-214, 2012.
DOI : 10.1093/bioinformatics/btp030

T. Jaakkola, M. Diekhans, and D. Haussler, A Discriminative Framework for Detecting Remote Protein Homologies, Journal of Computational Biology, vol.7, issue.1-2, pp.95-114, 1912.
DOI : 10.1089/10665270050081405

P. Jaccard, THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1, New Phytologist, vol.11, issue.2, pp.37-50, 1912.
DOI : 10.1111/j.1469-8137.1912.tb05611.x

E. Karsenti, S. G. Acinas, and P. Bork, A Holistic Approach to Marine Eco-Systems Biology, PLoS Biology, vol.6, issue.10, pp.1001177-1001196, 2011.
DOI : 10.1371/journal.pbio.1001177.g002

URL : https://hal.archives-ouvertes.fr/hal-00691580

L. Kaufman and P. J. Rousseeuw, Clustering by means of medoids In : Statistical Data Analysis Based on the L1-Norm and Related Methods. Sous la dir, pp.405-416, 1987.

M. Kimura, A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences, Journal of Molecular Evolution, vol.206, issue.5, Nov., pp.111-120, 1980.
DOI : 10.1038/scientificamerican1179-98

A. Kleiner, A. Talwalkar, P. Sarkar, and M. I. Jordan, A scalable bootstrap for massive data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.90, issue.4, pp.795-816, 2014.
DOI : 10.1007/978-1-4757-2545-2

URL : http://arxiv.org/pdf/1112.5016

T. Kohohen and P. J. Somervuo, Self-organizing maps of symbol strings, Neurocomputing, vol.21, issue.1-3, pp.19-30, 1998.
DOI : 10.1016/S0925-2312(98)00031-9

T. Kohonen, MATLAB Implementations and Applications of the Self-Organizing Map, p.48, 2014.

T. Kohonen, Self-Organizing Maps, 3rd Edition. T. 30, pp.32-48, 2001.

T. Kohonen and P. J. Somervuo, How to make large self-organizing maps for nonvectorial data, Neural Networks, vol.15, issue.8-9, pp.945-952, 2002.
DOI : 10.1016/S0893-6080(02)00069-2

N. Vessela, O. Kristensen, L. Christian, and H. G. Russnes, Principles and methods of integrative genomic analyses in cancer, Nature Reviews Cancer, vol.14, pp.299-313, 2014.

S. Kumar, M. Mohri, and A. Talwalkar, Sampling techniques for the Nyström method, In : Journal of Machine Learning Research, vol.13, pp.981-1006, 2012.

P. Langfelder, B. Zhang, and S. Horvath, Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R, Bioinformatics, vol.24, issue.5, pp.719-720, 2008.
DOI : 10.1093/bioinformatics/btm563

N. Laptev, K. Zeng, and C. Zaniolo, Early accurate results for advanced analytics on MapReduce, Proceedings of the 28th International Conference on Very Large Data Bases, pp.27-31
DOI : 10.14778/2336664.2336675

K. W. Lau, H. Yin, and S. Hubbard, Kernel self-organising maps for classification, Neurocomputing, vol.69, issue.16-18, pp.2033-2040, 2006.
DOI : 10.1016/j.neucom.2005.10.003

L. Christine, E. Yves, S. Robert, and T. Pierre, The ACT (STATIS method), Computational Statistics & Data Analysis, vol.181, issue.102, pp.97-119, 1994.

K. A. L?el?e, M. E. Cao, V. A. Costello, and . Lakis, mixMC : a multivariate statistical framework to gain insight into microbial communities, PloS One, vol.118, pp.160169-100, 2016.

J. A. Lee and M. Verleysen, Nonlinear Dimensionality Reduction Information Science and Statistics, pp.29-31, 2007.

C. Leslie, E. Eskin, A. Cohen, J. Weston, W. Stafford et al., Mismatch string kernels for discriminative protein classification, Bioinformatics, vol.20, issue.4, pp.467-476, 2004.
DOI : 10.1093/bioinformatics/btg431

URL : https://academic.oup.com/bioinformatics/article-pdf/20/4/467/476867/btg431.pdf

C. Leslie, E. Eskin, and W. S. Noble, THE SPECTRUM KERNEL: A STRING KERNEL FOR SVM PROTEIN CLASSIFICATION, Biocomputing 2002, pp.2-7, 2002.
DOI : 10.1142/9789812799623_0053

L. Gipsi, F. Karoline, and H. Nicolas, Determinants of community structure in the global plankton interactome, 2015.

Y. Lin, T. Liu, and C. Fuh, Multiple Kernel Learning for Dimensionality Reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.6, pp.1147-1160, 2010.
DOI : 10.1109/TPAMI.2010.183

C. Lozupone and R. Knight, UniFrac: a New Phylogenetic Method for Comparing Microbial Communities, Applied and Environmental Microbiology, vol.71, issue.12, pp.8228-8235, 2005.
DOI : 10.1128/AEM.71.12.8228-8235.2005

URL : http://aem.asm.org/content/71/12/8228.full.pdf

C. A. Lozupone, M. Hamady, S. T. Kelley, and R. Knight, Quantitative and Qualitative ?? Diversity Measures Lead to Different Insights into Factors That Structure Microbial Communities, Applied and Environmental Microbiology, vol.73, issue.5, pp.1576-1585, 2007.
DOI : 10.1128/AEM.01996-06

URL : http://aem.asm.org/content/73/5/1576.full.pdf

D. , M. Donald, and C. Fyfe, The kernel self organising map In : Proceedings of 4th International Conference on knowledge-based intelligence engineering systems and applied technologies, pp.317-320, 2000.

S. Mandal, W. Van, T. , and R. White, Analysis of composition of microbiomes: a novel method for studying microbial composition, Microbial Ecology in Health & Disease, vol.26, issue.0, pp.27663-100, 2015.
DOI : 10.1128/AEM.03006-05

J. Mariette, N. Villa-vialaneixe, M. J. Merényi, O. Mendenhall, and P. T. Driscoll, Aggregating Self-organizing maps with topology preservation Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2016) (6?8 jan. 2016) Sous la dir Advances in Intelligent Systems and Computing, pp.27-37, 2016.

J. Mariette and N. Villa-vialaneix, Unsupervised multiple kernel learning for heterogeneous data integration, btx682 (cf. p. 9, pp.39-41, 2017.
DOI : 10.1093/bioinformatics/btx682

J. Mariette, F. Rossi, M. Olteanu, and N. Villa-vialaneix, Accelerating stochastic kernel SOM Sous la dir. de M. VERLEYSEN. Bruges, Belgium : d-side publications, XXVth European Symposium on Artificial Neural Networks, pp.269-274, 2017.

J. Mariette, M. Olteanu, and N. Villa-vialaneix, Efficient interpretable variants of online SOM for large dissimilarity data, Neurocomputing, vol.225, issue.32, pp.31-48, 2017.
DOI : 10.1016/j.neucom.2016.11.014

URL : https://hal.archives-ouvertes.fr/hal-01465340

]. S. Bibliographie110, M. Massoni, N. Olteanu, and . Villa-vialaneix, Which distance use when extracting typologies in sequence analysis ? An application to school to work transitions, International Work Conference on Artificial Neural Networks (IWANN 2013). (12?14 juin 2013, pp.2013-62

A. Frederick, . Matsen, . Iv, N. Steven, and . Evans, Edge Principal Components and Squash Clustering : Using the Special Structure of Phylogenetic Placement Data for Sample Comparison, PLOS ONE, vol.83, pp.1-15

J. Mcauley and J. Leskovec, Learning to discover social circles in ego networks, NIPS Workshop on Social Network and Social Media Analysis. 2012 (cf, p.66
DOI : 10.1145/2556612

J. Paul, . Mcmurdie, and H. Susan, Waste Not, Want Not : Why Rarefying Microbiome Data Is Inadmissible, PLOS Computational Biology, vol.10, issue.4, pp.1-12, 2014.

P. J. Mcmurdie and S. Holmes, phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data, PLoS ONE, vol.473, issue.4, pp.61217-105, 2013.
DOI : 10.1371/journal.pone.0061217.s002

X. Meng, Scalable simple random sampling and stratified sampling, Proceedings of the 30th International Conference on Machine Learning (ICML 2013). T. 28. JMLR : W&CP. Georgia, USA, pp.2013-71

C. P. Meyer and G. Paulay, DNA Barcoding: Error Rates Based on Comprehensive Sampling, nov. 2005) (cf, p.58
DOI : 10.1371/journal.pbio.0030422.st003

URL : https://doi.org/10.1371/journal.pbio.0030422

F. Meyer, D. Paarmann, and M. D. Souza, The metagenomics RAST server ??? a public resource for the automatic phylogenetic and functional analysis of metagenomes, BMC Bioinformatics, vol.9, issue.1, p.24, 2008.
DOI : 10.1186/1471-2105-9-386

S. Needleman and C. Wunsch, A general method applicable to the search for similarities in the amino acid sequence of two proteins, Journal of Molecular Biology, vol.483, pp.443-453, 1970.

A. Neme, J. R. Pulido, A. Muñoz, S. Hern´andezhern´andez, and T. Dey, Stylistics analysis and authorship attribution algorithms based on self-organizing maps, Neurocomputing, vol.147, pp.147-159, 2012.
DOI : 10.1016/j.neucom.2014.03.064

M. E. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical Review E, vol.65, issue.2, pp.26113-67, 2004.
DOI : 10.1103/PhysRevE.68.065103

M. Niranjan and F. Fallside, Neural networks and radial basis functions in classifying static speech patterns, Computer Speech & Language, vol.4, issue.3, pp.275-289, 1990.
DOI : 10.1016/0885-2308(90)90009-U

T. M. Nye, Principal components analysis in the space of phylogenetic trees, The Annals of Statistics, vol.39, issue.5, pp.2716-2739, 2011.
DOI : 10.1214/11-AOS915SUPP

M. Olteanu and N. Villa-vialaneix, On-line relational and multiple relational SOM, Neurocomputing, vol.147, issue.70, pp.15-30, 2015.
DOI : 10.1016/j.neucom.2013.11.047

URL : https://hal.archives-ouvertes.fr/hal-01063831

M. Olteanu, N. Villa-vialaneix, M. J. Merényi, O. Mendenhall, and P. T. Driscoll, Sparse online self-organizing maps for large relational data Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2016) Advances in Intelligent Systems and Computing, pp.27-37, 2016.

M. Olteanu, N. Villa-vialaneix, and C. Cierco-ayrolles, Multiple kernel self-organizing maps, XXIst European Symposium on Artificial Neural Networks Sous la dir. de M. VERLEYSEN. Bruges, Belgium : d-side publications, pp.83-88, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00817920

M. Olteanu, N. Villa-vialaneix, and M. Cottrell, On-Line Relational SOM for Dissimilarity Data, Advances in Self-Organizing Maps (Proceedings of WSOM 2012, pp.12-14
DOI : 10.1007/978-3-642-35230-0_2

URL : https://hal.archives-ouvertes.fr/hal-00768948

P. A. Sous-la-dir.-de, J. Estevez, P. Principe, G. Zegers, and . T. Barreto, AISC (Advances in Intelligent Systems and Computing), pp.13-22

R. Ounit, S. Wanamaker, T. J. Close, and S. Lonardi, CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers, BMC Genomics, vol.7, issue.1-2, pp.236-256, 2015.
DOI : 10.1089/10665270050081478

L. A. Pasa, J. A. Costa, M. Guerra-de, M. , M. Polycarpou et al., Fusion of Kohonen maps ranked by cluster validity indexes Sous la dir, Proceedings of the 9th International Conference on Hybrid Artificial Intelligence Systems, pp.654-665, 2014.

J. N. Paulson, S. Colin, C. Hector, . Bravo, and P. Mihai, Robust methods for differential abundance analysis in marker gene surveys, Nature Methods, vol.1012, pp.1200-1202, 2013.
DOI : 10.1038/nmeth.2658

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4010126/pdf

S. Pavoine, A guide through a family of phylogenetic dissimilarity measures among sites, Oikos, vol.50, issue.12, pp.1719-1732, 2016.
DOI : 10.1007/BF00344966

URL : https://hal.archives-ouvertes.fr/hal-01307102

S. Pavoine, A. B. Dufour, and D. Chessel, From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis, Journal of Theoretical Biology, vol.228, issue.4, pp.523-537, 2004.
DOI : 10.1016/j.jtbi.2004.02.014

URL : https://hal.archives-ouvertes.fr/hal-00427622

B. S. Penn, Using self-organizing maps to visualize high-dimensional data, Computers & Geosciences, vol.31, issue.5, pp.531-544, 2005.
DOI : 10.1016/j.cageo.2004.10.009

L. Petrakieva and C. Fyfe, Bagging and Bumping Self Organising Maps, Computing and Information Systems Journal, vol.9, pp.69-77, 2003.

H. L. Hermier-des and P. , Structuration des tableauxàtableaux`tableauxà trois indices de la statistique, Thèse detroisì eme cycle Thèse de doct, p.42, 1976.

G. Polzlbauer-de, J. Paralic, G. Polzlbauer, and A. R. Sliezsky-dom, Survey and comparison of quality measures for self-organizing maps Sous la dir, Proceedings of the Fifth Workshop on Data Analysis, pp.67-82, 2004.

M. P¨olzlbauerp¨olzlbauer, M. Dittenbach, and A. Rauber, Advanced visualization of self-organizing maps with vector fields Advances in Self Organising Maps - WSOM'05, In : Neural Networks, vol.19, pp.6-7, 2006.

P. Sean, S. Damian, and T. Kalliopi, eggNOG v3.0 : orthologous groups covering 1133 organisms at 41 different taxonomic ranges, Nucleic Acids Research, vol.40, pp.1-284, 2012.

M. N. Price, P. S. Dehal, and A. P. Arkin, FastTree 2 ??? Approximately Maximum-Likelihood Trees for Large Alignments, PLoS ONE, vol.5, issue.3, pp.9490-106, 2010.
DOI : 10.1371/journal.pone.0009490.s003

URL : http://doi.org/10.1371/journal.pone.0009490

V. Pylro, F. Roesch, and D. K. Morais, Data analysis for 16S microbial profiling from different benchtop sequencing platforms, Journal of Microbiological Methods, vol.107, pp.30-37, 2014.
DOI : 10.1016/j.mimet.2014.08.018

URL : https://doi.org/10.1016/j.mimet.2014.08.018

Q. Christian, P. Elmar, and Y. Pelin, The SILVA ribosomal RNA gene database project : improved data processing and web-based tools, Nucleic Acids Research, vol.41, pp.590-596, 2013.

T. W. Randolph, S. Zhao, W. Copeland, M. Hullar, and A. Shojaie, Kernel-penalized regression for analysis of microbiome data

]. S. Bibliographie142, P. Ren, M. Ling, Y. Yang, Z. Ni et al., Multi-kernel PCA with discriminant manifold for hoist monitoring, Journal of Applied Sciences, vol.1320, pp.4195-4200, 2013.

DOI : 10.1111/j.0014-3820.2001.tb00731.x

URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1558-5646.2009.00804.x/pdf

F. Reverter, E. Vegas, and J. Oller, Kernel-PCA data integration with enhanced interpretability, BMC Systems Biology, vol.8, issue.Suppl 2, p.105, 2014.
DOI : 10.1142/S0218339009002831

URL : https://bmcsystbiol.biomedcentral.com/track/pdf/10.1186/1752-0509-8-S2-S6?site=bmcsystbiol.biomedcentral.com

S. Del, R. , V. L´opezl´opez, J. M. Beni´itezbeni´itez, and F. Herrera, On the use of MapReduce for imbalanced big data using Random Forest, Information Sciences, vol.285, pp.112-137, 2014.
DOI : 10.1016/j.ins.2014.03.043

M. D. Ritchie, E. R. Holzinger, R. Li, S. A. Pendergrass, and D. Kim, Methods of integrating data to uncover genotype???phenotype interactions, Nature Reviews Genetics, vol.58, issue.2, pp.36-37, 2015.
DOI : 10.1038/nature06758

P. Robert and Y. Escoufier, A Unifying Tool for Linear Multivariate Statistical Methods: The RV- Coefficient, Applied Statistics, vol.25, issue.3, pp.257-265, 1976.
DOI : 10.2307/2347233

F. Rossi-de, T. Villmann, F. M. Schleif, M. Kaden, and M. T. Lange, In : Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014) (2?4 juil. 2014) Sous la dir Advances in Intelligent Systems and Computing, pp.3-23

F. Rossi and N. Villa-vialaneix, Optimizing an organized modularity measure for topographic graph clustering: A deterministic annealing approach, Neurocomputing, vol.73, issue.7-9, pp.1142-1163, 2010.
DOI : 10.1016/j.neucom.2009.11.023

URL : https://hal.archives-ouvertes.fr/hal-00515892

F. Rossi, A. Hasenfuss, and B. Hammer, Accelerating relational clustering algorithms with sparse prototype representation, Proceedings of the 6th Workshop on Self-Organizing Maps (WSOM 07, pp.30-73, 2007.

R. Simon, R. Jennifer, . Brum, E. Bas, and . Dutilh, Ecogenomics and biogeochemical impacts of uncultivated globally abundant ocean viruses, Nature, vol.537, issue.107, pp.689-693, 2016.

R. Simon, T. Jeremy, M. Antoine, D. Debroas, and F. Enault, Metavir 2 : new tools for viral metagenome comparison and assembled virome analysis, BMC bioinformatics, vol.76, pp.15-24, 2014.

C. Saavedra, R. Salas, S. Moreno, and H. Allende, Fusion of Self Organizing Maps, Proceedings of the 9th International Work-Conference on Artificial Neural Networks, p.48, 2007.
DOI : 10.1007/978-3-540-73007-1_28

A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof, On-line Random Forests, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp.1393-1400, 2009.
DOI : 10.1109/ICCVW.2009.5457447

P. Sarlin and S. R¨onnqvistr¨onnqvist, Cluster Coloring of the Self-Organizing Map: An Information Visualization Perspective, 2013 17th International Conference on Information Visualisation, pp.532-538, 2013.
DOI : 10.1109/IV.2013.72

P. D. Schloss, S. L. Westcott, and T. Ryabin, Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities, Applied and Environmental Microbiology, vol.75, issue.23, pp.7537-7541, 2009.
DOI : 10.1128/AEM.01541-09

URL : http://aem.asm.org/content/75/23/7537.full.pdf

B. Sch¨olkopfsch¨olkopf, K. Tsuda, and J. P. Vert, Kernel methods in computational biology, p.29, 2004.

B. Sch¨olkopfsch¨olkopf, A. Smola, and K. R. M¨ullerm¨uller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

C. E. Shannon, A Mathematical Theory of Communication, Bell System Technical Journal, vol.27, issue.3, pp.379-423, 1948.
DOI : 10.1002/j.1538-7305.1948.tb01338.x

R. Shen, A. B. Olshen, and M. Ladanyi, Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis, Bioinformatics, vol.2225, pp.2906-2918, 2009.

E. H. Simpson, Measurement of Diversity, Nature, vol.163, issue.4148, pp.688-710, 1949.
DOI : 10.1038/163688a0

A. Singh, B. Gautier, and C. P. Shannon, DIABLO-an integrative, multi-omics, multivariate method for multi-group classification, In : BioRxiv, p.37, 2016.
DOI : 10.1101/067611

A. J. Smola and R. Kondor, Kernels and Regularization on Graphs, Proceedings of the Conference on Learning Theory (COLT) and Kernel Workshop, pp.144-158, 2003.
DOI : 10.1007/978-3-540-45167-9_12

URL : http://mlg.anu.edu.au/~smola/./papers/SmoKon03.ps

T. Sørensen, A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons, Kongelige Danske Videnskabernes Selskab, vol.45, pp.1-34, 1948.

S. Sunagawa, L. P. Coelho, and S. Chaffron, Structure and function of the global ocean microbiome Sous la dir, pp.106-114, 2015.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, series B, vol.581, pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

W. S. Togerson, Theory & Methods of Scaling, 1958.

S. Warren and . Torgerson, Metabolic sensing by p53 : keeping the balance between life and death, Psychometrika, vol.174, pp.401-419, 1952.

G. Towell and J. W. Shavlik, Interpretation of artificial neural networks : mapping knowledgebased neural networks into rules, Proceedings of Advances in Neural Information Processing Systems, p.52, 1992.

C. De, V. , S. Audic, and N. Henry, Eukaryotic plankton diversity in the sunlit ocean Sous la dir, pp.106-108, 2015.

S. Vega-pons and J. Ruiz-schulcloper, A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS, International Journal of Pattern Recognition and Artificial Intelligence, vol.8, issue.03, p.37, 2011.
DOI : 10.1016/j.knosys.2005.11.003

J. P. Vert, A tree kernel to analyse phylogenetic profiles, Bioinformatics, vol.18, issue.Suppl 1, pp.276-284, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S276

URL : https://hal.archives-ouvertes.fr/hal-00433591

N. Villa and F. Rossi, A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph (3?6 sept, 6th International Workshop on Self-Organizing Maps (WSOM), p.64, 2007.

V. Emilie, G. K. Farrant, and F. Michael, Environmental characteristics of Agulhas rings affect interocean plankton transport, 2015.

B. L. Vrusias, L. Vomvoridis, and L. Gillam, Distributing SOM Ensemble Training using Grid Middleware, 2007 International Joint Conference on Neural Networks, pp.2712-2717, 2007.
DOI : 10.1109/IJCNN.2007.4371387

J. Wagner, F. Chelar, and J. Kancherl, Metaviz : interactive statistical and visual analysis of metagenomic data, In : BioRxiv, p.24, 2017.
DOI : 10.1101/105205

]. Z. Wang, S. Chen, and T. Sun, MultiK-MHKS : a novel multiple kernel learning algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.302, pp.348-353, 2008.

J. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963.
DOI : 10.1007/BF02289263

H. White, Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples, PLoS Computational Biology, vol.33, issue.4, pp.1-11, 2009.
DOI : 10.1371/journal.pcbi.1000352.s001

URL : https://doi.org/10.1371/journal.pcbi.1000352

M. Van-de, W. , M. Neerincx, T. Buffart, D. Sie et al., ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs, BMC Bioinformatics, vol.15, issue.1, p.22, 2014.
DOI : 10.1111/biom.12036

C. K. Williams and M. Seeger, Using the Nyström method to speed up kernel machines, Advances in Neural Information Processing Systems (Proceedings of NIPS : Neural Information Processing Systems Foundation, pp.35-71, 2000.

D. E. Wood and S. L. Salzberg, Kraken: ultrafast metagenomic sequence classification using exact alignments, R46 (cf, p.20, 2014.
DOI : 10.1186/1471-2105-12-385

URL : https://genomebiology.biomedcentral.com/track/pdf/10.1186/gb-2014-15-3-r46?site=genomebiology.biomedcentral.com

D. Gary, . Wu, C. Jun, and H. Christian, Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes, In : Science, vol.3346052, pp.105-108, 2011.

D. Yan, L. Huang, M. I. Jordan, J. Elder, F. Soulié-fogelman et al., Fast approximate spectral clustering Sous la dir, Proceedings of the 15th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, pp.907-916, 2009.
DOI : 10.1145/1557019.1557118

Z. Yu, P. Luo, and J. You, Incremental semi-supervised clustering ensemble for high dimensional data clustering, IEEE Transactions on Knowledge and Data Engineering, vol.283, pp.701-714, 2016.
DOI : 10.1109/icde.2016.7498386

Z. Yu, J. You, L. Li, H. S. Wong, and G. Han, Representative Distance: A New Similarity Measure for Class Discovery From Gene Expression Data, IEEE Transactions on NanoBioscience, vol.11, issue.4, pp.341-351, 2012.
DOI : 10.1109/TNB.2012.2208198

Z. Yu, H. S. Wong, J. You, and G. Han, Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme, Information Sciences, vol.203, pp.83-101, 2012.
DOI : 10.1016/j.ins.2012.03.012

B. Zhao, J. T. Kwok, C. Zhang-de, C. Apte, H. Park et al., Multiple kernel clustering Sous la dir, Proceedings of the 2009 SIAM International Conference on Data Mining (SDM), pp.638-649, 2009.
DOI : 10.1137/1.9781611972795.55

URL : http://www.cs.ust.hk/~jamesk/papers/sdm09.pdf

X. Zhu, A. Gisbrecht, F. M. Schleif, and B. Hammer, Approximation techniques for clustering dissimilarity data, Neurocomputing, vol.90, pp.72-84, 2012.
DOI : 10.1016/j.neucom.2012.01.033

J. Zhuang, J. Wang, S. C. Hoi, and X. Lan, Unsupervised multiple kernel clustering, Journal of Machine Learning Research : Workshop and Conference Proceedings, pp.129-144, 2011.