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.
Lan Huong Nguyen, Dan Iter, Robert Bierman