Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.

ABSTRACT: Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequ

SUBMITTER: Gilliot PA 

PROVIDER: S-EPMC11260469 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature