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            "description": "Nous avons le plaisir de vous annoncer la tenue d'une formation sur l'analyse de données metabarcoding en mai 2018.\nCelle-ci vous proposera : \n- une formation complète à l'outil FROGS sous Galaxy\n- l'intervention de plusieurs experts du domaine avec exposés thématiques et tutoriaux \n- le tout sur 5j, du 14 au 18 mai prochain\nCette semaine thématique est co-organisée entre la cellule bioinformatique de l’ifremer à Brest, la plateforme MIGALE de Jouy et  la plate-forme ABiMS de Roscoff qui accueillera la formation.\nRetrouver les détails du programme ici : \nhttp://tiny.ifremer.fr/formation-metabarcoding-2018\nSi cette formation vous intéresse, merci de bien vouloir compléter le formulaire d'inscription (disponible dans le  lien ci-dessus).\n----------------------------------------\nWe are pleased to announce a training on metabarcoding data analysis in May 2018.\nThis one will propose to you:\n- complete training in the FROGS tool under Galaxy\n- the intervention of several experts in the field with thematic presentations and tutorials\n- all on  5days , from May 14 to 18\nThis theme week is co-organized with the IFREMER bioinformatic team (Brest) , the Migale bioinformatic platform(Jouy en Josas)  and  the ABiMS (Roscoff) bioinformatic platform and would take place in Roscoff..\nFind the details of the program here:\nhttp://tiny.ifremer.fr/formation-metabarcoding-2018\nIf you are interested in this training, please complete the registration form (see link above).\nTrainning will be in French with slides in English.\n",
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            "description": "With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially in recent times and a major issue is to mine useful knowledge from these data which are also heterogeneous in nature. Machine learning (ML) is a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine learning can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics.\r\n\r\nThis 2-day course will introduce participants to the machine learning taxonomy and the applications of common machine learning algorithms to omics data. The course will cover the common methods being used to analyse different omics data sets by providing a practical context through the use of basic but widely used R libraries. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets.",
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