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- University of Cambridge Bioinformatics Training75
- iAnn1
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Scientific topic
- Data mining
- Bioinformatics1156
- Genome annotation369
- Exomes366
- Genomes366
- Genomics366
- Personal genomics366
- Synthetic genomics366
- Viral genomics366
- Whole genomes366
- Biological modelling304
- Biological system modelling304
- Systems biology304
- Systems modelling304
- Biomedical research258
- Clinical medicine258
- Experimental medicine258
- General medicine258
- Internal medicine258
- Medicine258
- Bottom-up proteomics169
- Discovery proteomics169
- MS-based targeted proteomics169
- MS-based untargeted proteomics169
- Metaproteomics169
- Peptide identification169
- Protein and peptide identification169
- Proteomics169
- Quantitative proteomics169
- Targeted proteomics169
- Top-down proteomics169
- Data visualisation135
- Data rendering128
- Exometabolomics95
- LC-MS-based metabolomics95
- MS-based metabolomics95
- MS-based targeted metabolomics95
- MS-based untargeted metabolomics95
- Mass spectrometry-based metabolomics95
- Metabolites95
- Metabolome95
- Metabolomics95
- Metabonomics95
- NMR-based metabolomics95
- Data management94
- Metadata management94
- Aerobiology90
- Behavioural biology90
- Biological rhythms90
- Biological science90
- Biology90
- Chronobiology90
- Cryobiology90
- Reproductive biology90
- Computational pharmacology84
- Immunology84
- Pharmacoinformatics84
- Pharmacology84
- Pattern recognition79
- Comparative transcriptomics70
- Transcriptome70
- Transcriptomics70
- Cloud computing61
- Computer science61
- HPC61
- High performance computing61
- High-performance computing61
- Functional genomics44
- Biomathematics43
- Computational biology43
- Mathematical biology43
- Theoretical biology43
- Epigenomics42
- Disease41
- Pathology41
- Active learning40
- Ensembl learning40
- Kernel methods40
- Knowledge representation40
- Machine learning40
- Neural networks40
- Recommender system40
- Reinforcement learning40
- Supervised learning40
- Unsupervised learning40
- Metagenomics35
- Shotgun metagenomics35
- RNA-Seq analysis33
- Electrophysiology30
- Physiology30
- Pipelines30
- Software integration30
- Tool integration30
- Tool interoperability30
- Workflows30
- High-throughput sequencing29
- Chromosome walking28
- Clone verification28
- DNA-Seq28
- DNase-Seq28
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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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