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Keyword
- CWL2
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- Introduction bioinformatics1
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- 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
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Scientific topic
- Active learning3
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Venue
- Adriatico Guest House - Denardo Lecture Hall International Centre for Theoretical Physics Trieste, Italy2
- Athena Research & Innovation Center in Information Communication & Knowledge Technologies, 6, Artemidos2
- University of Arkansas Rome Center: Palazzo Taverna, Via di Monte Giordano, 36 - 00186 Rome Italy.1
- Computational and Data Driven Science1
- FORTH1
- Instituto Gulbenkian de Ciência1
- National Hellenic Research Foundation, 48, Leoforos Vasileos Konstantinou1
- Online1
- Pasteur Hellenic Institute, 127 Vasilissis Sofias1
- Rotterdam1
- Seminar Room Institute of Applied Biosciences (INAB) Centre for Research & Technology - Hellas (CERTH) 6th km Charilaou - Thermis rd1
- Seminar Room Institute of Applied Biosciences (INAB) Centre for Research & Technology - Hellas (CERTH) 6th km Charilaou - Thermis rd1
- University of Basel Kollegienhaus Petersplatz 1 CH-4001 Basel1
- Virtual (Slack)1
- Wellcome Genome Campus1
- Zoom1
- “ATHENA” Research Center (ATHENA RC) Artemidos 6 & Epidavrou 15125, Marousi, GR1
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Target audience
- Master students3
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- post-docs3
- Operators, developers and managers of bioinformatics resources2
- Researchers2
- Academia1
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- ELIXIR members1
- Industry professionals, and people with a keen interest in the subject.1
- Investigators and researchers working in all areas of the life sciences.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
- PostDocs1
- Professors1
- Scientists1
- Technicians1
- 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
- beginner bioinformaticians1
- bioinformaticians1
- intermediate bioinformaticians1
- students1
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Language
- English5
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