In this competition,we tried to predict degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position. We will then score your models on a second generation of RNA sequences that have just been devised by Eterna players for COVID-19 mRNA vaccines. These final test sequences are currently being synthesized and experimentally characterized at Stanford University in parallel to your modeling efforts.
We used Transformer/GRU/LSTM/Wavenet to predict. This kernel was used as a baseline for LSTM/GRU part https://www.kaggle.com/xhlulu/openvaccine-simple-gru-model. This was done as a team by Reza Vaghefi, Urvish Patel and Mizanur Rahman.