Using variational autoencoders and gaussian processes to forecast the protein sequence of influenza.
As part of my Insight project, I built Flu Forecaster, a project that aims to forecast influenza sequence evolution using deep learning. In it, I used a combination of variational autoencoders (VAEs) to translate time-stamped influenza protein sequences into a continuous coordinate space, and then used gaussian process regression to forecast future continuous coordinates that could be translated back to sequence space.