Dynamic Latent Manifolds for Biologically Aware Genomic Compression
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Solution Overview
Problem
Current genomic data compression methods fail to leverage biological structure and functional relationships, leading to suboptimal performance, loss of critical information, and analytical inefficiencies, while lacking privacy-preserving collaboration tools and intelligent memory management.
Innovation Solution
A system and method using dynamic latent manifolds that transform genomic data into geometric representations, embedding it with semantic relationships and adaptive compression rates based on biological significance, supporting hierarchical organization and privacy-preserving collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If general-purpose compression algorithms (gzip, CRAM) are used on genomic data, then compression is achieved, but biological structure and functional relationships are not preserved
Solution Approach 1:
The patent applies different compression strategies to different regions of genomic data based on their biological importance. Critical regions with high biological significance are preserved with higher fidelity, while less critical regions undergo more aggressive compression. This localized quality approach ensures that biologically important information is retained while achieving overall compression efficiency.
Solution Approach 2:
The system dynamically adjusts compression parameters based on the biological properties of different genomic regions. By changing compression parameters (such as compression level, block size, and retention thresholds) according to local biological characteristics, the system optimizes both compression ratio and preservation of biological information simultaneously.
2Reliability
If entire genomic datasets are loaded into memory for processing, then comprehensive analysis is possible, but memory bottlenecks occur preventing real-time analysis
Solution Approach 1:
The patent divides large genomic datasets into smaller manageable segments or blocks that can be processed individually in memory. This segmentation allows the system to handle comprehensive datasets without loading everything into memory simultaneously, enabling real-time processing while maintaining analysis completeness through systematic segment-by-segment processing.
Solution Approach 2:
The system performs preliminary indexing, feature extraction, and metadata generation on genomic data before main processing. This preliminary action creates efficient data structures and summaries that allow rapid querying and analysis without requiring the entire dataset to be in memory during actual analysis operations.
3Measurement precision
If separate computational pipelines process different genomic data types, then each data type is handled with specialized methods, but correlations between DNA sequences, expression patterns, and protein measurements are missed
Solution Approach 1:
The patent merges multiple separate computational pipelines into a unified integrated framework that processes DNA sequences, gene expression profiles, and protein measurements simultaneously. This integration preserves the specialized processing capabilities for each data type while adding the ability to detect and utilize correlations across different omics layers, preventing loss of inter-omics relationship information.
Solution Approach 2:
The system implements a universal processing framework that can handle multiple genomic data types through a common architecture. This multi-functional system applies data-type-specific algorithms where needed while maintaining the ability to analyze cross-data-type relationships, achieving both specialized accuracy and integrated correlation detection.
4Reliability
If access controls and data use agreements protect genomic data privacy, then data security is maintained, but collaboration is limited and scientific progress slows
Solution Approach 1:
The patent introduces cryptographic intermediaries and trusted execution environments that enable collaborative genomic analysis without requiring direct access to raw sensitive data. These intermediaries allow multiple researchers to jointly analyze data and share results while maintaining privacy protections, thus enabling collaboration without compromising security.
Solution Approach 2:
The system creates and shares encrypted copies or derived representations of genomic data that preserve analytical utility while protecting sensitive information. Researchers can work with these protected copies to collaboratively derive insights without accessing or exposing the original sensitive genomic data, maintaining both privacy and collaboration efficiency.
Data Source
AI summary
A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.