Genomic copy number evolution in cancer refers to the acquisition or loss of genome segments over time due to mutational processes in cancer that result in the loss of biological mechanisms for the maintenance of genome integrity in cells. Genome sequencing can allow us to detect these copy number alterations in cancer cells but do not directly inform us what the sequence of evolutionary events was that led to the present state of the cancer. We have developed a novel, first-of-its-kind reinforcement learning based approach for inferring evolutionary trajectories from genomic copy number profiles which we call \RLEvolution. We show that \RLEvolution is able to deconvolve the sequence of complex events that may occur during cancer evolution using a combination of simulated and real-world cancer datasets.


Christopher Yau
Professor of Artificial Intelligence

I am Professor of Artificial Intelligence. I am interested in statistical machine learning and its applications in the biomedical sciences.