Cancer results from an evolutionary process that yields a heterogeneous tumor with distinct subpopulations and varying sets of somatic mutations. This perspective discusses computational methods to infer models of evolutionary processes in cancer that aim to improve our understanding of tumorigenesis and ultimately enhance current clinical practice.
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