Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure.
Here we consider three recently proposed deep generative frameworks for protein design: (AR) the sequence-based autoregressive generative model, (GVP) the precise structure-based graph neural network, and Fold2Seq that leverages a fuzzy and scale-free representation of a three-dimensional fold, while enforcing structure-to-sequence (and vice versa) consistency.
We apply extreme value theory on the new formal robustness guarantee and the estimated robustness is called second-order CLEVER score.
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems.