DeepCor analyzes the strategies followed by SARS-CoV-2 for its survival and evolution. The idea is based on the concepts of reinforcement learning (RL) where the virus acts as the agent and tries to survive in the environment by taking some actions. The focus of the work is on the genome of SARS-CoV-2 that how certain genomic changes help the virus to adapt. The RL algorithms in DeepCor are based on policy gradient methods, actor critic methods or Q learning methods.  The second part of this project focuses on the rates at which the virus changes its behavior, which is done by the help of recurrent neural networks (RNNs).

This work is supported by the Technical University of Kaiserslautern.
Deadline: December 2023