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MOBSTER - Model-based subclonal clustering in cancer - combines the theory of Population Genetics with standard Machine Learning in order to learn the clonal structure of a cancer population from read counts of bulk sequencing.
Compared to previous methods, MOBSTER exploits the distributions that we know to arise from cancer's growth, and computes better fits that can improve our current evolutionary analysis of bulk samples.