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- University of Cambridge Bioinformatics Training110
- iAnn1
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Scientific topic
- Pattern recognition
- Bioinformatics1099
- Genome annotation302
- Exomes299
- Genomes299
- Genomics299
- Personal genomics299
- Synthetic genomics299
- Viral genomics299
- Whole genomes299
- Biological modelling250
- Biological system modelling250
- Systems biology250
- Systems modelling250
- Biomedical research219
- Clinical medicine219
- Experimental medicine219
- General medicine219
- Internal medicine219
- Medicine219
- Data visualisation185
- Data rendering178
- Bottom-up proteomics152
- Discovery proteomics152
- MS-based targeted proteomics152
- MS-based untargeted proteomics152
- Metaproteomics152
- Peptide identification152
- Protein and peptide identification152
- Proteomics152
- Quantitative proteomics152
- Targeted proteomics152
- Top-down proteomics152
- Data management125
- Metadata management125
- Data mining113
- Aerobiology98
- Behavioural biology98
- Biological rhythms98
- Biological science98
- Biology98
- Chronobiology98
- Cryobiology98
- Reproductive biology98
- Comparative transcriptomics90
- Transcriptome90
- Transcriptomics90
- Exometabolomics75
- LC-MS-based metabolomics75
- MS-based metabolomics75
- MS-based targeted metabolomics75
- MS-based untargeted metabolomics75
- Mass spectrometry-based metabolomics75
- Metabolites75
- Metabolome75
- Metabolomics75
- Metabonomics75
- NMR-based metabolomics75
- Functional genomics65
- Immunology65
- Cloud computing60
- Computer science60
- HPC60
- High performance computing60
- High-performance computing60
- Computational pharmacology59
- Pharmacoinformatics59
- Pharmacology59
- Active learning50
- Ensembl learning50
- Kernel methods50
- Knowledge representation50
- Machine learning50
- Neural networks50
- Recommender system50
- Reinforcement learning50
- Supervised learning50
- Unsupervised learning50
- Biomathematics47
- Computational biology47
- Mathematical biology47
- Theoretical biology47
- RNA-Seq analysis41
- Pipelines40
- Software integration40
- Tool integration40
- Tool interoperability40
- Workflows40
- Data curation38
- Database curation38
- High-throughput sequencing36
- Chromosome walking35
- Clone verification35
- DNA-Seq35
- DNase-Seq35
- High throughput sequencing35
- NGS35
- NGS data analysis35
- Next gen sequencing35
- Next generation sequencing35
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Target audience
- Graduate students92
- Institutions and other external Institutions or individuals90
- Postdocs and Staff members from the University of Cambridge90
- 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.16
- Everyone is welcome to attend the courses9
- please review the policies.9
- <span style="color:#FF0000">After you have booked a place7
- if you are unable to attend any of the live sessions and would like to work in your own time7
- including for registered university students.<span style="color:#FF0000">7
- please email the Team as Attendance will be taken on all courses. A charge is applied for non-attendance7
- Note that we will not cover specific topics in phylogenomics (whole-genome phylogenies) or bacterial genomics.5
- This course is aimed at researchers with no prior experience in phylogenetic analysis who would like an introduction to the foundations of building phylogenies from relatively small sequences (viral genomes and/or targeted regions of eukaryotic genomes).5
- Anyone who is using sequencing as part of their work and/or research.4
- Researchers who are applying or planning to apply image analysis in their research4
- Researchers who want to extract quantitative information from microscopy images4
- Institutions and other external institutions or individuals.3
- Postdocs and other Research Staff from the University of Cambridge3
- The course is aimed at biologists interested in microbiology3
- This introductory course is aimed at biologists with little or no experience in machine learning.3
- analysis of complex microbiomes and antimicrobial resistance.3
- prokaryotic genomics3
- The course is aimed primarily at mid-career scientists – especially those whose formal education likely included statistics2
- This course is aimed at researchers with no prior experience in the analysis of ChIP-seq data2
- 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 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
- beginner bioinformaticians1
- or who are already GATK users seeking to improve their understanding of and proficiency with the tools.1
- seeking an introductory course into the tools1
- who would like to get started in processing their data and perform downstream analysis and visualisation of their results.1
- who would like to get started in processing their data using a standardised pipeline and perform downstream analysis and visualisation of their results.1
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