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Keyword
- HDRUK19
- machine learning6
- Bioimage analysis4
- Electron microscopy4
- Light microscopy4
- Machine learning models4
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- Artificial Intelligence2
- BioImage Archive2
- BioStudies Database2
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- R-programming1
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- Evolutinary genomics1
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- Literature (literature)1
- Machine Learning and Artificial Intelligence Course1
- Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression1
- Metabolomics1
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- Systems biology, Pathway analysis, Network analysis, Microarray data analysis, Nanomaterials1
- Transcriptomics1
- algorithms1
- biomedical applications1
- biostatistics1
- clinical genomics1
- data mining and analysis1
- dynamic simulations1
- genomics1
- mutational landscapes1
- transcriptomics1
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Scientific topic
- Machine learning
- Bioinformatics1026
- Genome annotation285
- Exomes282
- Genomes282
- Genomics282
- Personal genomics282
- Synthetic genomics282
- Viral genomics282
- Whole genomes282
- Biological modelling236
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- Biomedical research194
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- Experimental medicine194
- General medicine194
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- Medicine194
- Bottom-up proteomics143
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- MS-based targeted proteomics143
- MS-based untargeted proteomics143
- Metaproteomics143
- Peptide identification143
- Protein and peptide identification143
- Proteomics143
- Quantitative proteomics143
- Targeted proteomics143
- Top-down proteomics143
- Data visualisation133
- Data rendering126
- Data management92
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- Research data management (RDM)92
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- MS-based metabolomics67
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- Mass spectrometry-based metabolomics67
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- RNA-Seq analysis33
- Epigenomics31
- Disease29
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- High-throughput sequencing28
- Pipelines28
- Protein databases28
- Software integration28
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- Tool interoperability28
- Workflows28
- Chromosome walking27
- Clone verification27
- DNA-Seq27
- DNase-Seq27
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Venue
- Craik-Marshall Building19
- European Bioinformatics Institute, Hinxton3
- Computational and Data Driven Science1
- Fondazione Edmund Mach, Palazzo della Ricerca e della Conoscenza, Via E. Mach 1, San Michele all'Adige1
- Instituto Gulbenkian de Ciência1
- La Pedrera, 92, Passeig de Gràcia1
- Provinciehuis Provincie Vlaams-Brabant Provincieplein 1 3010 Leuven Belgium1
- Provinciehuis, Provincieplein 1, Leuven1
- TBC1
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Target audience
- Institutions and other external Institutions or individuals19
- Postdocs and Staff members from the University of Cambridge19
- Graduate students17
- This is aimed at life scientists with little or no experience in machine learning and that are looking at implementing these approaches in their research.13
- This introductory course is aimed at biologists with little or no experience in machine learning.3
- Computational biologists2
- PhD students2
- Researchers2
- Students and researchers from life-sciences or biomedical backgrounds2
- The course is open to Graduate students2
- or will shortly have2
- post-docs2
- the need to apply the techniques presented during the course to biomedical data.2
- who have2
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £350. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £175. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £400. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £200. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- Bioinformaticians1
- Computer science1
- Experimental Researchers1
- Life Science Researchers1
- Master students1
- PhD Students1
- Plant research1
- Postdoctoral students1
- 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
- bioinformaticians1
- biological data analysts1
- students1
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