Unsupervised annotation of differences between genomic datasets

dentifying differences between genomic data sets can help us study diseases better and find their treatments. In this project, we trained autoencoders on HiC-maps of human cells and tested them on colon cells. HiC maps are genomic wide contact maps that tell us the relationship each entity has with its environment. We experimented with a simplified and a regularized autoencoder which produced a result of 0.022 and 0.021 training losses respectively. We then integrated Concept saliency map to highlight pixels that were relevant for a prediction by our model. Our model was able to highlight key areas of differences between a treated and an untreated cancerous human colon cell.