Unsupervised cross-domain translation via deep learning and adversarial attention neural networks and application to music-inspired protein designs.

ABSTRACT: Taking inspiration from nature about how to design materials has been a fruitful approach, used by humans for millennia. In this paper we report a method that allows us to discover how patterns in disparate domains can be reversibly related using a computationally rigorous approach, the AttentionCrossTranslation model. The algorithm discovers cycle- and self-consistent relationships and offers a bidirectional translation of information across disparate knowledge domains. The approach is validated with a set of known translation problems, and then used to discover a mapping between musical data-based on the corpus of note sequences in J.S. Bach's Goldberg Variations created in 1741-and protein sequence data-information sampled more recently. Using protein folding algorithms, 3D structures o

SUBMITTER: Buehler MJ 

PROVIDER: S-EPMC10028431 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature