Logic programming applied to genome evolution in cancer

As often observed in the literature, cancer evolution follows a path that is unique to each patient; therefore, classical analysis based on the identification of typical mutations, provides little insight in the understanding of the general rules that drive cancer genesis and evolution. Recent genome sequencing pipelines allow researchers to retrieve rich genetic and epigenetic information from sampled tissues. Analyzing and comparing the evolution of cancer cells for each patient over a large time span can provide some accurate information and relationships. This paper presents a project for a logic programming based analysis that processes time-related genomic information.