Determining distances and consensus between trees

The mutational heterogeneity of a person's tumors can be described as a tree. Therefore, understanding tree similarities is challenging to predict similarities in the cancer evolution of different patients. In this work, we present an approach to determine distances between trees, associating their Newick format and SWAP operations in strings. We also consider a set of trees as an input and work with the MEDIAN and CLOSE problems, which are ways of relating all inputs as the problems of minimizing the sum and minimizing the maximum, respectively. In addition, we developed two algorithms to obtain upper limits for the MEDIAN and CLOSE solutions, and presented some experiments.