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

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

This project aimed to demonstrate how deep learning could be leveraged for studying DNA replication spatiotemporal dynamics. Through well-characterised cellular models, we showed in both supervised and unsupervised settings how convolutional neural networks could successfully identify aberrant DNA r...

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Bibliographische Detailangaben
Hauptverfasser: Ng-Kee-Kwong, Julian, Chen, Naiming, Buonomo, Sara
Format: Dataset
Sprache:eng
Schlagworte:
Biological Sciences::Molecular Biology Biophysics and Biochemistry
Convolutional neural network
Cyclin E
Deep learning
DNA replication
Machine learning
Replication timing
RIF1
Self-supervised learning
Spatiotemporal organisation
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Beschreibung
Zusammenfassung:This project aimed to demonstrate how deep learning could be leveraged for studying DNA replication spatiotemporal dynamics. Through well-characterised cellular models, we showed in both supervised and unsupervised settings how convolutional neural networks could successfully identify aberrant DNA replication dynamics, while additionally reconstructing progression through S-phase. We make the associated image datasets available here. Please refer to README.txt for further information.
DOI:10.7488/ds/7754