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Content provider
- ELIXIR Portugal20
- ELIXIR Slovenia5
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Keyword
- Data management plan7
- EeLP6
- eLearning6
- training4
- Data managment plan3
- Genomics3
- Proteomics3
- life sciences3
- Assembly2
- Bioinformatics2
- Computer science2
- Training2
- data management2
- Statistics1
- metabolic modelling, Genome, open-source software platform1
- #bioinformatics hashtag#pangenomics hashtag#data hashtag#analysis hashtag#genomicdata hashtag#genomics hashtag#course1
- Computational Pangenomics1
- Course design1
- Course development1
- Gadgets, software 1
- Integrative1
- Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression1
- Mass Spectroscopy1
- TADbit1
- Teaching1
- bioinformatics1
- data annotation1
- data stewardship1
- genomics1
- high-performance computing1
- online learning1
- pedagogy1
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Scientific topic
- Data management10
- Metadata management10
- Exomes4
- Genome annotation4
- Genomes4
- Genomics4
- Personal genomics4
- Synthetic genomics4
- Viral genomics4
- Whole genomes4
- Bioinformatics3
- Cloud computing3
- Computer science3
- HPC3
- High performance computing3
- High-performance computing3
- Active learning1
- Bayesian methods1
- Biomathematics1
- Biostatistics1
- Computational biology1
- Data curation1
- Data provenance1
- Data submission, annotation, and curation1
- Database curation1
- Descriptive statistics1
- Ensembl learning1
- FAIR data1
- Findable, accessible, interoperable, reusable data1
- Gaussian processes1
- Inferential statistics1
- Kernel methods1
- Knowledge representation1
- Machine learning1
- Markov processes1
- Mathematical biology1
- Multivariate statistics1
- Neural networks1
- Probabilistic graphical model1
- Probability1
- Proteogenomics1
- Recommender system1
- Reinforcement learning1
- Statistics1
- Statistics and probability1
- Supervised learning1
- Theoretical biology1
- Unsupervised learning1
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Venue
- Instituto Gulbenkian de Ciência (IGC), 6, Rua Quinta Grande4
- Instituto Gulbenkian de Ciência3
- R. Q.ta Grande 6, 6, Rua Quinta Grande2
- University of Ljubljana, Faculty of Medicine2
- University of Minho - Campus of Gualtar, Rua da Universidade2
- Champalimaud Foundation, Avenida Brasília1
- Cirad, 389, Avenue Agropolis1
- Escuela Técnica Superior de Ingeniería Informática, 35, Bulevar Louis Pasteur1
- ITQB NOVA, Avenida da República1
- Instituto de Medicina Molecular (IMM), Avenida Professor Egas Moniz1
- Online and F2F (See details in training documentation)1
- Rua da Quinta Grande, 61
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Organizer
- BioData.pt7
- Pedro Fernandes7
- ELIXIR Slovenia2
- BioData.pt | ELIXIR Portugal1
- Biodata.pt; Congento; RNEM - Portuguese Mass Spectrometry Network1
- CCMAR / BioData.pt1
- ELIXIR PT, Instituto Gulbenkian1
- ELIXIR-BE, ELIXIR-FR, ELIXIR-NO, ELIXIR-PT, ELIXIR-SE, ELIXIR-SI1
- Escuela Técnica Superior de Ingeniería Informática1
- GTPB - IGC1
- Instituto Gulbenkian, ELIXIR Portugal, GOBLET1
- Pedro L Fernandes1
- The Gulbenkian Training Programme in Bioinformatics1
- The Gulbenkian Training Programme in Bioinformatics, Biodata.pt - Elixir's portuguese node of the european project1
- The Gulbenkian Training Programme in Bioinformatics, Biodata.pt - Elixir's portuguese node of the european project1
- Universidade do Minho1
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Target audience
- PhD students12
- Life Science Researchers10
- Master students8
- Researchers8
- Technicians8
- life scientists5
- PhD Students2
- Researchers in Life Sciences1
- Bioinformaticians and wet-lab biologists who can program in Perl.1
- Biologists and bioinformaticians who are dealing with high-throughput gene expression data or other high-throughput data and would like to learn state-of-the-art methods for mining and analysing such data.1
- Biologists and bioinformaticians. The course will be of particular interest to researchers investigating organisms without a reference genome or populations featuring high levels of genetic diversity.1
- Biologists, Genomicists, Computer Scientists1
- Graduate Students1
- Scientists1
- This course is oriented towards biologists and bioinformaticians. The course will be of particular interest to researchers investigating organisms without a reference genome or populations featuring high levels of genetic diversity.1
- This training course is aimed at researchers who are not expert in proteomics and want to integrate quantitative proteomics results into wider biomedical experiments.1
- bioinformaticians1
- computational and statistical techniques are used to identify and validate the causal links between targets1
- everyone who is interested in learning to use metabolic modelling in their research, using user-friendly tools. No programming skills are required.1
- experimental researchers and bioinformaticians at the graduate and post-graduate levels1
- post-docs1
- post-doctoral researchers and principle investigators working in wet-lab biology. Participants who are looking for a basic introduction to the bioinformatics resources offered by the EMBL-EBI and want to know more about accessing biological data and tools.1
- students1
- teachers1
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