Adaptive Genomic Data Compression With Neural Recovery of Lost Information
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Solution Overview
Problem
Existing genomic data compression methods face challenges in balancing lossless and lossy compression, leading to information loss and scalability issues, particularly in handling diverse genomic data, which is crucial for maintaining biological significance and downstream analysis.
Innovation Solution
A system and method using a neural network for upsampling decompressed genomic data, integrating a quality analysis engine and a rate control engine, with a novel AI deblocking network comprising recurrent layers and a channel-wise transformer to capture inter-channel dependencies, mitigating compression artifacts and enhancing data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lossless compression is used to retain all original information, then information完整性 is improved, but compression ratio deteriorates
Solution Approach 1:
The patent segments genomic data into different importance categories (critical regions vs. non-critical regions) and applies different compression strategies to each segment. Critical regions undergo lossless compression to preserve information完整性, while non-critical regions use lossy compression to achieve higher compression ratios, thus resolving the contradiction between information retention and compression efficiency
Solution Approach 2:
The patent applies local quality by using a quality analysis engine to identify and prioritize specific genomic regions based on their biological importance. Different quality levels are assigned to different regions, allowing the system to maintain high information fidelity where needed while accepting higher compression elsewhere, effectively balancing information retention with compression ratio
2Productivity
If lossy compression is used to achieve higher compression ratios, then compression ratio is improved, but information loss worsens
Solution Approach 1:
The patent introduces an intermediary neural network recovery system that acts as a mediator between lossy compression and final data usage. The neural network is trained to predict and recover lost information from compressed data, effectively bridging the gap between compression efficiency and information fidelity, allowing high compression ratios while minimizing actual information loss
Solution Approach 2:
The patent implements feedback mechanisms where the quality analysis engine continuously evaluates compressed data quality and adjusts compression parameters accordingly. The system uses feedback from decomposition quality assessments to optimize the balance between compression ratio and information retention, dynamically adjusting strategies based on observed performance
3Productivity
If compression algorithms are optimized for specific data types, then compression efficiency is improved, but adaptability to diverse genomic data deteriorates
Solution Approach 1:
The patent creates a universal compression system that can handle diverse genomic data types through multiple specialized engines working together. The quality analysis engine, rate control engine, and neural network recovery system form a multi-functional framework that adapts to different genomic data characteristics while maintaining consistent efficiency, resolving the contradiction between specialization and versatility
4Manufacturing precision
If compression artifacts are reduced through deblocking, then data quality is improved, but computational complexity worsens
Solution Approach 1:
The patent replaces traditional mechanical deblocking filters with a neural network-based recovery system. The neural network learns complex patterns and relationships in genomic data, providing superior artifact reduction and quality improvement while managing computational complexity through efficient network architecture and training strategies, effectively substituting conventional approaches with intelligent systems
Data Source
AI summary
A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.