Representation Learning to Integrate and Interpret Omics Data

Sara Masarone

Abstract:

The last decade has seen an increase in the amount of high throughput data available to researchers. While this has allowed scientists to explore various hypotheses and research questions, it has also highlighted the importance of data integration in order to facilitate knowledge extraction and discovery. Although many strategies have been developed over the last few years, integrating data whilst generating an interpretable embedding still remains challenging due to difficulty in regularisation, especially with deep generative models. Thus, we introduce a framework called Regularised Multi-View Variational Autoencoder (RMV-VAE) to integrate different omics data types whilst allowing researchers to obtain more biologically meaningful embeddings.

* This work is under consideration*