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As the cost and throughput of genomic technologies reach a point where DNA sequencing is close to becoming a routine exam at the clinics, there is a lot of hope that treatments of diseases like cancer can dramatically improve by a digital revolution in medicine, where smart algorithms analyze « big medical data » to help doctors take the best decisions for each patient. The application of machine learning-based techniques to genomic data raises however numerous computational and mathematical challenges that I will illustrate on a few examples of cancer patient stratification from gene expression or somatic mutation profiles.
