Abstract
Allele frequency is the relative frequency of a variant at a particular locus, and larger cellular occupancy of a mutation is associated with earlier mutations. RankNet is a machine learning algorithm that aim to reduce pairwise ranking errors. We use it to infer the right order of mutation occurrence. Since the evolutionary order of gene mutations by only one sample is not informative to any causal relationship, we use multiple samples to infer the causal relationship of those mutations. We proposed a procedure to solve this problem. Our first step is to use Neighbor joining method to constructed an unrooted tree according to the allele frequencies of mutations. We then find the optimal tree by choosing a root with minimum rank error rate. The aim of this thesis is to use the mutation allele frequencies from multiple samples to infer the the right order of mutation occurrence and reconstruct the most likely phylogenetic tree for the recurrent mutations of cancer cells.