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- HDRUK19
- Bioimage analysis4
- Electron microscopy4
- Light microscopy4
- Machine learning models4
- Scientific computing4
- BioImage Archive2
- BioStudies Database2
- Data integration2
- Deep learning2
- Electron Microscopy Public Image Archive - EMPIAR2
- Logic modelling2
- MOFA2
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- Artificial Intelligence1
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- ChEMBL: Bioactive data for drug discovery1
- Crop improvement1
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- Europe PubMed Central1
- GPU1
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- Personalised medicine1
- Plant webinar series1
- Protein Data Bank in Europe1
- Single cell1
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Scientific topic
- Neural networks
- Bioinformatics799
- Genome annotation239
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- Biomedical research194
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- Medicine194
- Bottom-up proteomics138
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- MS-based targeted proteomics138
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- Peptide identification138
- Protein and peptide identification138
- Proteomics138
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- Top-down proteomics138
- Data visualisation116
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- Aerobiology82
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- Exometabolomics56
- LC-MS-based metabolomics56
- MS-based metabolomics56
- MS-based targeted metabolomics56
- MS-based untargeted metabolomics56
- Mass spectrometry-based metabolomics56
- Metabolites56
- Metabolome56
- Metabolomics56
- Metabonomics56
- NMR-based metabolomics56
- Immunology49
- Functional genomics43
- Data management34
- Metadata management34
- Research data management (RDM)34
- Active learning32
- Ensembl learning32
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- Protein bioinformatics27
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- Proteins27
- Metagenomics25
- Shotgun metagenomics25
- Protein structure24
- Bioimaging21
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- Cloud computing20
- Computer science20
- HPC20
- High performance computing20
- High-performance computing20
- Antimicrobial stewardship19
- Electrophysiology19
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- Microbial genetics19
- Microbial physiology19
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- Workshops and courses32
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- United Kingdom23
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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
- Students and researchers from life-sciences or biomedical backgrounds2
- The course is open to Graduate students2
- or will shortly have2
- 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
- Plant research1
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