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A cancer bioinformatics workshop spanning genome alignment, somatic alterations, single-cell genomics, gene expression, clinical data, and multi-omic integration.
An introductory R workshop progresses from first scripts through t-tests, loops, functions, linear models, and an R Markdown lab.
Learn to identify over-represented pathways, visualize and analyze gene networks, study regulatory networks, and predict gene function from gene lists.
Hands-on introduction to machine learning using decision trees, neural networks, and hidden Markov models in gene-finding and protein secondary-structure labs.
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