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Related Experiment Video

Updated: Sep 18, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

INNSE: Invertible neural network-based DNA image storage with self-correction encoding.

Yanfen Zheng¹, Xiaokang Zhang¹, Lijun Sun¹

¹School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China.

Computational and Structural Biotechnology Journal

|June 23, 2025

Summary

This summary is machine-generated.

DNA data storage is improved with a new invertible neural network method (INNSE). This approach enhances encoding density and error correction for images and videos, outperforming existing algorithms.

Keywords:

Codebook generator DNA storage Invertible neural network Self-correction

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Area of Science:

Biotechnology
Data Storage
Bioinformatics

Background:

DNA data storage offers a high-density alternative to traditional silicon-based media.
Current DNA storage methods struggle with multimodal data, exhibiting limitations in encoding density, time complexity, and error correction for insertions/deletions.

Purpose of the Study:

To develop an advanced DNA data storage method that overcomes the limitations of existing techniques for multimodal data.
To improve encoding density, reduce time complexity, and enhance error correction capabilities in DNA storage systems.

Main Methods:

Proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE).
Utilized network invertibility for up-down sampling of image and video data to minimize DNA sequence requirements.
Designed a codebook generator for encoding low-dimensional vectors into constrained DNA sequences.
Implemented a self-correction mechanism for base-level DNA sequence error correction without redundant information.

Main Results:

INNSE achieved a 3x increase in encoding density compared to the Yin-Yang algorithm.
Encoding time complexity was reduced by 95.26% with the INNSE method.
Under a 2% simulated error rate, INNSE improved PSNR by 80.88% and SSIM by 63.82%.

Conclusions:

INNSE demonstrates significant advantages in encoding performance and error correction ability for DNA data storage.
The proposed method effectively addresses challenges in storing multimodal data, offering a more efficient and robust solution.
INNSE represents a substantial advancement in making DNA data storage practical for complex data types.