Machine learning plays an important role in cancer research. In this talk, we’ll tackle the challenge of predicting which patients are likely to respond to given anti-cancer treatments. In doing so, we’ll show how tools such as Snakemake/Bioconda can be used to create reproducible workflows and illustrate the challenges of interpreting predictive models in large, highly-correlated feature spaces.
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