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Keyword
- bioinformatics3
- DNA-seq2
- Data analysis2
- NGS2
- NGS bioinformatics2
- Variant calling2
- metabolic modelling, Genome, open-source software platform1
- Bisulfite-Seq1
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- Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression1
- MetQuest1
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Scientific topic
- Bioinformatics8
- Biological sequences3
- Chromosome walking3
- Clone verification3
- DNA-Seq3
- DNase-Seq3
- High throughput sequencing3
- High-throughput sequencing3
- MicroRNA sequencing3
- NGS3
- NGS data analysis3
- Next gen sequencing3
- Next generation sequencing3
- Panels3
- Primer walking3
- RNA sequencing3
- RNA-Seq3
- RNA-Seq analysis3
- Sanger sequencing3
- Sequence analysis3
- Sequence databases3
- Sequencing3
- Small RNA sequencing3
- Small RNA-Seq3
- Small-Seq3
- Targeted next-generation sequencing panels3
- Transcriptome profiling3
- WTSS3
- Whole transcriptome shotgun sequencing3
- miRNA-seq3
- Active learning2
- Data management2
- Drug discovery2
- Ensembl learning2
- Kernel methods2
- Knowledge representation2
- Machine learning2
- Metadata management2
- Neural networks2
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- Reinforcement learning2
- Research data management (RDM)2
- Supervised learning2
- Unsupervised learning2
- Bayesian methods1
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- Metabolites1
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Venue
- Earlham Institute (EI), Colney Lane7
- 17. listopadu 1192/12, 12, 17. listopadu1
- Instituto Gulbenkian de Ciência1
- Instituto Gulbenkian de Ciência (IGC), 6, Rua Quinta Grande1
- Ole-Johan Dahls hus, 23B, Gaustadalléen1
- PC-COLLEGE Berlin, 78, Stresemannstraße1
- Postdoc Centre, 16, Mill Lane1
- Procida1
- Průmyslová, Průmyslová1
- Technická 1903/3, 3, Technická1
- University of Melbourne1
- Virtual (Slack)1
- online1
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Target audience
- post-docs
- Graduate students927
- Institutions and other external Institutions or individuals570
- Postdocs and Staff members from the University of Cambridge570
- PhD students446
- Academics356
- Industry355
- PhD353
- Researchers189
- Everyone is welcome to attend the courses98
- please review the policies.97
- Life Science Researchers64
- Biologists45
- as well as other departmental training within the University of Cambridge (potentially under a different name) so participants who have attended statistics training elsewhere should check before applying.39
- Beginner37
- bioinformaticians37
- Master students32
- This course is included as part of several DTP and MPhil programmes30
- Students28
- PhD Students25
- Institutions and other external Institutions or individuals 24
- Postdocs and Staff members from the University of Cambridge24
- All postgraduates23
- <span style="color:#FF0000">After you have booked a place22
- Post Docs22
- if you are unable to attend any of the live sessions and would like to work in your own time22
- including for registered university students.<span style="color:#FF0000">22
- please email the Team as Attendance will be taken on all courses. A charge is applied for non-attendance22
- This course is aimed at students and researchers of any background.21
- We assume no prior knowledge of what a HPC is or how to use it.21
- life scientists17
- Bioinformaticians16
- It may be particularly useful for those who have attended other Facility Courses and now need to process their data on a Linux server. It will also benefit those who find themselves using their personal computers to run computationally demanding analysis/simulations and would like to learn how to adapt these to run on a HPC.16
- Professors16
- 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
- mixed audience16
- PhD candidates15
- PhD candidate14
- Technicians14
- PI13
- Bioinformaticians and wet-lab biologists who can program12
- Biologists, Genomicists, Computer Scientists12
- Graduate Students12
- This course is open to everyone who is interested. Have a look at our guidelines.12
- Existing R users who are not familiar with dplyr and ggplot211
- Postdoctoral Fellows11
- The course is aimed at biologists interested in microbiology11
- Those with programming experience in other languages that want to know what R can offer them11
- researchers11
- Molecular Biologists10
- Undergraduate students10
- Wet-lab researchers and bioinformaticians10
- data stewards10
- postdoctoral researchers10
- Beginners9
- Engineers9
- This course is - in abbreviated form - included as part of several DTP and MPhil programmes9
- data managers9
- data steward / data manager9
- Biomedical researchers8
- Clinicians8
- Data managers8
- Data stewards8
- postdocs8
- software developers, bioinformaticians8
- 2025/26 Wellcome Sanger Institute student cohort and staff members7
- Anyone who is using sequencing as part of their work and/or research.7
- DMP writers7
- Life Scientists7
- Note that we will not cover specific topics in phylogenomics (whole-genome phylogenies) or bacterial genomics.7
- Postdoctoral Researchers7
- Researcher in life sciences7
- Researchers who are applying or planning to apply image analysis in their research7
- Senior scientist/ Principal investigator7
- 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).7
- This workshop is aimed at researchers interested in proteins7
- Trainers7
- but not essential as we will walk through a MS typical experiment and data as part of learning about the tools.7
- network analysis7
- Anyone intersted in GWAS and using the H3Africa genotyping chip6
- Biologists and bioinformaticians6
- Clinical Scientists6
- Familiarity with mass spectrometry or proteomics in general is desirable6
- Masters students6
- Medical Device6
- Post-Docs6
- Postdoctoral researchers6
- Postgraduate students6
- Principal Investigators6
- Researcher6
- Scientists6
- The course is aimed primarily at mid-career scientists – especially those whose formal education likely included statistics6
- The course is targeted to either proteomics practitioners or data analysts/bioinformaticians that would like to learn how to use R to analyse proteomics data.6
- Training Designers6
- but who have not perhaps put this into practice since.6
- prokaryotic genomics and antimicrobial resistance.6
- protein-protein interactions and related areas6
- Attendees of our training courses on specific applications (e.g. RNA-seq5
- Bioinformatician5
- ChIP-seq5
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