Neural Upsampling for Genomic Data Lossy Compression Recovery
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Solution Overview
Problem
Current data compression techniques, particularly lossy compression, result in data loss that is challenging to recover, especially in genomic data, where information retention is critical for accurate analysis and scalability with advancing sequencing technologies.
Innovation Solution
A neural network system that incorporates a novel AI deblocking network with recurrent layers for feature extraction and a channel-wise transformer with attention to recover lost information from decompressed genomic data, enhancing compression quality and preserving crucial information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression is applied to genomic data, then compression ratio is improved, but information loss occurs
Solution Approach 1:
A neural network is introduced as an intermediary component between the lossy compression and decompression processes. The neural network learns to predict and recover lost genomic information by analyzing patterns in the compressed data, effectively mediating the information loss caused by lossy compression while maintaining high compression ratios
Solution Approach 2:
The system changes the parameters of the decompression process by using learned parameters from the neural network instead of traditional decompression algorithms. The neural network adjusts recovery parameters dynamically based on the specific genomic data patterns, enabling effective information recovery at varying compression levels
2Loss of information
If lossless compression is applied to genomic data, then information retention is improved, but compression ratio deteriorates
Solution Approach 1:
The compression system is segmented into two distinct stages: a lossy compression stage that achieves high compression ratios, and a neural network-based recovery stage that restores information. This segmentation allows each stage to optimize for its specific function, combining the benefits of both lossy compression and information retention
3Productivity
If compression is applied to genomic data, then data scalability is improved, but data quality deteriorates
Solution Approach 1:
The neural network is trained using feedback from the original uncompressed genomic data. During training, the network receives compressed data as input and is guided by the original high-quality data to learn accurate recovery patterns, creating a feedback loop that continuously improves data quality while maintaining scalability
Data Source
AI summary
A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.