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- Metabolomics1
- R-programming1
- C13 Labeling Fluxomics1
- CWL1
- Docker1
- 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
- Systems biology 1
- Workflows1
- biostatistics1
- machine learning1
- omics data1
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- Workshops and courses5
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Target audience
- post-docs2
- 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
- Master students1
- PhD Students1
- PhD students1
- Researchers1
- Scientists1
- 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
- students1
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