BioData.pt Business Forum: Advanced data management for added value
6 May 2019
Oeiras, PortugalBioData.pt Business Forum: Advanced data management for added value https://www.eventbrite.com/e/forum-biodatapt-empresas-gestao-avancada-de-dados-para-a-criacao-de-valor-tickets-58432946516?aff=eac2 https://tess.elixir-europe.org/events/biodata-pt-business-forum Research and development institutions annually produce zetabytes of biological information, of which only terabytes are used by the bio-industry. There is a large biological information, the big biodata, still to be explored by the bio-industry, which can add value into new products and services. The BioData.pt Business Forum: advanced data management for added value, organized by BioData.pt and PBio, will bring together four national companies related to the health sector, the sea, agriculture and bioindustry, and tell their stories to inspire new ideas and new businesses. There will also be an opportunity to discuss best practices in curation, management and use of biodata, as well as the use of bioinformatics tools essential to its success. 2019-05-06 14:30:00 UTC 2019-05-06 18:00:00 UTC Biodata.pt Instituto Gulbenkian de Ciência (IGC), 6, Rua Quinta Grande, Oeiras, Portugal Instituto Gulbenkian de Ciência (IGC), 6, Rua Quinta Grande Oeiras Portugal 2780-156 Instituto Gulbenkian de CiênciaBiodata.pt - Elixir's portuguese node of the european projec firstname.lastname@example.org  Clinical ScientistsbioinformaticiansBiologists, Genomicists, Computer Scientistssoftware developers, bioinformaticiansbiology and bioinformatics sophomore undergraduatesbiocurators Institutions and other external Institutions or individualsIndustry health professionalsplant researchersBiomedical researchers studies human diseases or developmental biology 120 meetings_and_conferences  Biodata, Bioinformatics, Biodata
15 - 17 November 2021
Oeiras, PortugalMachine Learning http://biodata.pt https://tess.elixir-europe.org/events/machine-learning 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. This 3 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. Instructors: Vandrille Duchemin, University of Basel, CH Crhistian Cardona, University of Tuebingen, DE 2021-11-15 09:30:00 UTC 2021-11-17 18:00:00 UTC Pedro Fernandes Instituto Gulbenkian de Ciência, Oeiras, Portugal Instituto Gulbenkian de Ciência Oeiras Portugal 2780-156 Machine learning Instituto Gulbenkian de Ciência email@example.com To register your interest, please send an e-mail to firstname.lastname@example.org with "Machine Learning" in the subject line UNTIL November 11th 2021, stating the reason why you would be interested in attending this course in a single paragraph. ELIXIR PhD Studentspost-docsScientistsstudents 17 workshops_and_courses registration_of_interest Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression
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