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- University of Cambridge Bioinformatics Training75
- iAnn1
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Scientific topic
- Data mining
- Bioinformatics1026
- Genome annotation287
- Exomes284
- Genomes284
- Genomics284
- Personal genomics284
- Synthetic genomics284
- Viral genomics284
- Whole genomes284
- Biological modelling236
- Biological system modelling236
- Systems biology236
- Systems modelling236
- Biomedical research194
- Clinical medicine194
- Experimental medicine194
- General medicine194
- Internal medicine194
- Medicine194
- Bottom-up proteomics144
- Discovery proteomics144
- MS-based targeted proteomics144
- MS-based untargeted proteomics144
- Metaproteomics144
- Peptide identification144
- Protein and peptide identification144
- Proteomics144
- Quantitative proteomics144
- Targeted proteomics144
- Top-down proteomics144
- Data visualisation133
- Data rendering126
- Data management93
- Metadata management93
- Research data management (RDM)93
- Aerobiology89
- Behavioural biology89
- Biological rhythms89
- Biological science89
- Biology89
- Chronobiology89
- Cryobiology89
- Reproductive biology89
- Pattern recognition78
- Comparative transcriptomics68
- Transcriptome68
- Transcriptomics68
- Exometabolomics67
- LC-MS-based metabolomics67
- MS-based metabolomics67
- MS-based targeted metabolomics67
- MS-based untargeted metabolomics67
- Mass spectrometry-based metabolomics67
- Metabolites67
- Metabolome67
- Metabolomics67
- Metabonomics67
- NMR-based metabolomics67
- Computational pharmacology61
- Pharmacoinformatics61
- Pharmacology61
- Cloud computing59
- Computer science59
- HPC59
- High performance computing59
- High-performance computing59
- Biomathematics52
- Computational biology52
- Immunology52
- Mathematical biology52
- Theoretical biology52
- Active learning46
- Ensembl learning46
- Kernel methods46
- Knowledge representation46
- Machine learning46
- Neural networks46
- Recommender system46
- Reinforcement learning46
- Supervised learning46
- Unsupervised learning46
- Functional genomics44
- RNA-Seq analysis33
- Epigenomics31
- Disease29
- Metagenomics29
- Pathology29
- Shotgun metagenomics29
- High-throughput sequencing28
- Pipelines28
- Protein databases28
- Software integration28
- Tool integration28
- Tool interoperability28
- Workflows28
- Chromosome walking27
- Clone verification27
- DNA-Seq27
- DNase-Seq27
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Target audience
- Institutions and other external Institutions or individuals75
- Postdocs and Staff members from the University of Cambridge75
- Graduate students74
- 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
- Researchers who are applying or planning to apply image analysis in their research4
- Researchers who want to extract quantitative information from microscopy images4
- This introductory course is aimed at biologists with little or no experience in machine learning.3
- The course is aimed primarily at mid-career scientists – especially those whose formal education likely included statistics2
- but who have not perhaps put this into practice since.2
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Members of Industry to pay 575.00 GBP. </span style> <span style="color:#0000FF">All Members of the University of Cambridge1
- Affiliated Institutions and other academic participants from External Institutions and Charitable Organizations to pay 250.00 GBP. </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
- BioImage Analysts with some experience of basic microscopy image analysis1
- Biologists1
- Biomedical researchers1
- Biophysicists1
- Cell Biologists1
- Life Science Researchers1
- PhD Students or young researchers in molecular biology and/or genetics with little or no background in bioinformatics. 1
- The course is aimed at biologists interested in microbiology1
- The course is open to Graduate students1
- The handson component is aimed at novice to intermediate users who are seeking detailed guidance with GATK and related tools.1
- The lecture based component of the workshop is aimed at a mixed audience of people who are new to the topic of variant discovery or to GATK1
- This course is appropriate for researchers who are relatively proficient with computers but maybe not had the time or resources available to become programmers.1
- This course may be of interest to physical scientists looking to develop their knowledge of Python coding in the context of bioimage analysis1
- analysis of complex microbiomes and antimicrobial resistance.1
- beginner bioinformaticians1
- or who are already GATK users seeking to improve their understanding of and proficiency with the tools.1
- prokaryotic genomics1
- seeking an introductory course into the tools1
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