ArticleiScience2023
GCNSA: DNA storage encoding with a graph convolutional network and self-attention.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Storing Data With Propargylic Amines Using Thin-Layer Chromatography for the Data Retrieval.ChemPlusChem · 2026Article
- Article
- Coli bond: A dual-function encryption system for secure information storage and transmission by microorganisms.PloS one · 2025Article
- INNSE: Invertible neural network-based DNA image storage with self-correction encoding.Computational and structural biotechnology journal · 2025Article
- Levy Sooty Tern Optimization Algorithm Builds DNA Storage Coding Sets for Random Access.Entropy (Basel, Switzerland) · 2024Article
- Article
- DBTRG: De Bruijn Trim rotation graph encoding for reliable DNA storage.Computational and structural biotechnology journal · 2023Article
- Towards long double-stranded chains and robust DNA-based data storage using the random code system.Frontiers in genetics · 2023Article
- A universal lesion detection method based on partially supervised learning.Frontiers in pharmacology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA Encoding, as a key step in DNA storage, plays an important role in reading and writing accuracy and the storage error rate. However, currently, the encoding efficiency is not high enough and the encoding speed is not fast enough, which limits the performance of DNA storage systems. In this work, a DNA storage encoding system with a graph convolutional network and self-attention (GCNSA) is proposed. The experimental results show that DNA storage code constructed by GCNSA increases by 14.4% on average under the basic constraints, and by 5%-40% under other constraints. The increase of DNA storage codes effectively improves the storage density of 0.7-2.2% in the DNA storage system. The GCNSA predicted more DNA storage codes in less time while ensuring the quality of codes, which lays a foundation for higher read and write efficiency in DNA storage.
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Registered trials
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