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kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets
dc.contributor.author | Ramon, Elies | |
dc.contributor.author | Belanche-Muñoz, Lluís | |
dc.contributor.author | Molist, Francesc | |
dc.contributor.author | Quintanilla, Raquel | |
dc.contributor.author | Perez-Enciso, Miguel | |
dc.contributor.author | Ramayo-Caldas, Yuliaxis | |
dc.contributor.other | Producció Animal | ca |
dc.date.accessioned | 2021-09-28T10:23:11Z | |
dc.date.available | 2021-09-28T10:23:11Z | |
dc.date.issued | 2021-01-28 | |
dc.identifier.citation | Ramon, Elies, Lluís Belanche-Muñoz, Francesc Molist, Raquel Quintanilla, Miguel Perez-Enciso, and Yuliaxis Ramayo-Caldas. 2021. "Kernint: A Kernel Framework For Integrating Supervised And Unsupervised Analyses In Spatio-Temporal Metagenomic Datasets". Frontiers In Microbiology 12. doi:10.3389/fmicb.2021.609048. | ca |
dc.identifier.issn | 1664-302X | ca |
dc.identifier.uri | http://hdl.handle.net/20.500.12327/1355 | |
dc.description.abstract | The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github.com/elies-ramon/kernInt. | ca |
dc.format.extent | 14 | ca |
dc.language.iso | eng | ca |
dc.publisher | Frontiers Media | ca |
dc.relation.ispartof | Frontiers in Microbiology | ca |
dc.rights | Attribution 4.0 International | ca |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets | ca |
dc.type | info:eu-repo/semantics/article | ca |
dc.description.version | info:eu-repo/semantics/publishedVersion | ca |
dc.rights.accessLevel | info:eu-repo/semantics/openAccess | |
dc.embargo.terms | cap | ca |
dc.relation.projectID | MICIU-AEI/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I/PID2019-108829RB-I00/ES/LA BELLEZA DE LO PROFUNDO: APLICACIONES DEL DEEP LEARNING A LA PREDICCION GENOMICA/ | ca |
dc.relation.projectID | MINECO/Programa Estatal de I+D+I orientada a los retos de la sociedad/AGL2016-78709-R/ES/UTILIZACION DE SECUENCIAS COMPLETAS PARA LA MEJORA DE ESPECIES DOMESTICAS/ | ca |
dc.relation.projectID | MINECO/Programa Estatal de I+D+I orientada a los retos de la sociedad/AGL2017-88849-R/ES/MICROBIOTA INTESTINAL Y GENETICA DEL HUESPED: CONTRIBUCION CONJUNTA A LA EFICIENCIA, EL COMPORTAMIENTO Y LA ROBUSTEZ EN PORCINO/ | ca |
dc.relation.projectID | MICIU-FEDER/Programa Estatal de promoción del talento y su empleabilidad en I+D+I/RYC2019-027244-I/ES/Metagenomics and integrative biology tools to improve sustainable livestock systems/ | ca |
dc.relation.projectID | MINECO/Programa Estatal de fomento de la investigación científica y técnica de excelencia/SEV-2015-0533/ES/ / | ca |
dc.subject.udc | 619 | ca |
dc.identifier.doi | https://doi.org/10.3389/fmicb.2021.609048 | ca |
dc.contributor.group | Genètica i Millora Animal | ca |
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