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- NGS
- DNA-seq2
- NGS bioinformatics2
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- 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
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Scientific topic
- Bioinformatics2
- Biological sequences2
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- NGS2
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- Workshops and courses2
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Target audience
- post-docs
- Molecular Biologists3
- Pathologists3
- bioinformaticians3
- Biologists2
- Biologists, Genomicists, Computer Scientists2
- Biological sciences research students and postdocs who may want to use HPC in their research.Please note that Biochemistry first year graduate students book this course via their Moodle site not here.1
- Biologist1
- Cloud users1
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- PhD Students1
- PhD students1
- PhD students and young researchers in the life science and computational biology field who are planning to use RNA-seq data and are looking for the best practices to analyze these types of data1
- This course is aimed at PhD students and post-doctoral researchers who are applying or planning to apply high throughput sequencing technologies in cancer research and wish to familiarise themselves with bioinformatics tools and data analysis methodologies specific to cancer data. Familiarity with the technology and biological use cases of high throughput sequencing (HTS) is required, as is some experience with R/Bioconductor (basic understanding of the R syntax and ability to manipulate R objects) and the Unix/Linux operating system.1
- software engineers1
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