Algorithms for Cancer GenomicsNot offered this year
Offered most years, last taught:
This graduate seminar will explore algorithmic challenges that emerge in the analysis and interpretation of cancer genome sequencing data, with a focus on two major themes. The first theme is the mutational process of cancer evolution. The underlying algorithmic problem is to construct trees that represent the relationships between cells from mutational data. We will explore tree reconstruction algorithms using phylogenetic techniques (perfect phylogeny and Dollo parsimony) and population genetic techniques (branching processes and the coalescent). The second theme is the identification of combinations of cancer causing mutations. Such combinations typically result from biological interactions between genes, which are represented via graphs, or networks. We will examine algorithms to analyze data on graphs including random walks (e.g. PageRank), diffusion processes, community detection, and spectral methods for graph partitioning. The course will be organized in seminar style where students will read and present recent research papers and complete a class project.