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Keyword
- Metabolomics1
- C13 Labeling Fluxomics1
- CWL1
- Docker1
- ELIXIR-GOBLET Train-the-Trainer1
- Fluxomics1
- Genome-scale Metabolic Modeling 1
- Isotopic labeling1
- Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression1
- Mass Spectrometry - based fluxomics1
- Metabolic Network Analysis 1
- Metabolic Reaction Databases1
- Metabolic flux analysis1
- Metabolic pathway1
- Metabolism1
- NMR-based fluxomics1
- R-programming1
- Systems biology 1
- Workflows1
- biostatistics1
- machine learning1
- omics data1
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Scientific topic
- Active learning3
- Ensembl learning3
- Kernel methods3
- Knowledge representation3
- Machine learning3
- Neural networks3
- Recommender system3
- Reinforcement learning3
- Supervised learning3
- Unsupervised learning3
- Bioinformatics1
- Biomathematics1
- Computational biology1
- Mathematical biology1
- Omics1
- Theoretical biology1
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Venue
- Adriatico Guest House - Denardo Lecture Hall International Centre for Theoretical Physics Trieste, Italy1
- Computational and Data Driven Science1
- Instituto Gulbenkian de Ciência1
- Pasteur Hellenic Institute, 127 Vasilissis Sofias1
- Seminar Room Institute of Applied Biosciences (INAB) Centre for Research & Technology - Hellas (CERTH) 6th km Charilaou - Thermis rd1
- “ATHENA” Research Center (ATHENA RC) Artemidos 6 & Epidavrou 15125, Marousi, GR1
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Target audience
- Master students2
- post-docs2
- Academia1
- ELIXIR members1
- Industry professionals, and people with a keen interest in the subject.1
- Mainly graduate students and Junior post-docs, of either experimental or computational background, with basic training in life sciences, or (bio)chemistry, or physics, or mathematics, or engineering1
- PhD Students1
- PhD candidates1
- PhD students1
- Researchers1
- Scientists1
- This course is designed for scientists with some to little or no teaching experience1
- This course is intended for master and PhD students, post-docs and staff scientists familiar with different omics data technologies who are interested in applying machine learning to analyse these data. No prior knowledge of Machine Learning concepts and methods is expected nor required1
- Undergraduate students1
- students1
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Language
- English2
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