Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics.

ABSTRACT: In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous human diseases, the labour-intensive methods employed to study DNA replication have hindered large-scale analyses of its roles in pathological processes. In this study, we employ two distinct methodologies. We first apply supervised machine learning, successfully classifying S-phase patterns in wild-type mouse embryonic stem cells (mESCs), while additionally identifying altered replication dynamics in Rif1-deficient mESCs. Given the constraints imposed by a classification-based approach, we then develop an unsupervised method for large-scale detec

SUBMITTER: Ng-Kee-Kwong J 

PROVIDER: S-EPMC11865476 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature