Algorithms for identifying statistically significant progression patterns in cancer genomes

This work formalizes the problem of the identification of significant progression patterns from cancer genomics data and proposes algorithms to find such patterns. The proposed algorithms are based on Monte Carlo methods to find statistically relevant patterns using a probabilistic model that use tail distribution bounds to focus the computation on the most promising patterns. The proposed approach has been tested on real cancer data from The Cancer Genome Atlas project.