CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

ABSTRACT: Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two

SUBMITTER: Ren R 

PROVIDER: S-EPMC11311715 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature