GENE 245 Project Machine Learning for HiC data

In this work we apply machine learning as a conducive method towards identifying previously unstudied patterns in chromosome interaction data sets. We rst use supervised learning to show that patterns identi ed by a user can be learned by tensor ow models, and then transition into unsupervised methods to delve even more deeply into the possibilities of discovery without human intervention.