Active learning of enhancer and silencer regulatory grammar in photoreceptors.

ABSTRACT: Cis-regulatory elements (CREs) direct gene expression in health and disease, and models that can accurately predict their activities from DNA sequences are crucial for biomedicine. Deep learning represents one emerging strategy to model the regulatory grammar that relates CRE sequence to function. However, these models require training data on a scale that exceeds the number of CREs in the genome. We address this problem using active machine learning to iteratively train models on multiple rounds of synthetic DNA sequences assayed in live mammalian retinas. During each round of training the model actively selects sequence perturbations to assay, thereby efficiently generating informative training data. We iteratively trained a model that predicts the activities of sequences containi

SUBMITTER: Friedman RZ 

PROVIDER: S-EPMC10473580 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature