ArticleScientific reports2021
Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Towards density-optimal DNA storage with practical bijective coding.Nature communications · 2026Article
- Deep Learning Predicts Dissimilar DNA-DNA Binding and Engineers Hyperconnected Networks.Nature communications · 2026Article
- Accurate prediction of oligonucleotide probe hybridization efficiency via a transformer-based model.BMC genomics · 2026Article
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Modelling the melting of DNA oligomers with non-inert dangling ends.Frontiers in molecular biosciences · 2025Article
- Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting.Nature communications · 2024Article
- Deqformer: high-definition and scalable deep learning probe design method.Briefings in bioinformatics · 2024Article
- Modelling local and general quantum mechanical properties with attention-based pooling.Communications chemistry · 2023Article
- Article
- Nanopore Detection Assisted DNA Information Processing.Nanomaterials (Basel, Switzerland) · 2022Review
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Abstract
Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in solution, thus enabling search and pattern-matching capabilities. Being able to control and predict the process of DNA hybridisation is crucial for the ambitious future of Hybrid Molecular-Electronic Computing. Current tools are, however, limited in terms of throughput and applicability to large-scale problems. We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. For this purpose, we introduce an in silico-generated hybridisation dataset of over 2.5 million data points, enabling the use of deep learning. Depending on hardware, we achieve a reduction in inference time ranging from one to over two orders of magnitude compared to the state-of-the-art, while retaining high fidelity. We then discuss the integration of our methods in modern, scalable workflows.
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