Neural Network Upsampling for Lossy Genomic Data Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing genomic and proteomic data compression methods struggle to balance high compression ratios with the need to preserve biologically relevant information, particularly in lossy compression scenarios, and lack unified systems for efficient data processing, analysis, and integration.
Innovation Solution
A system and methods using neural networks for upsampling decompressed genomic and proteomic data, incorporating ProteomicUNet for proteomic data upsampling, ProteinFormer for 3D structure prediction, BindingSiteFinder for binding site prediction, and a deep learning system with a latent transformer core for large codeword models, to enhance data reconstruction and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression is applied to genomic data to achieve higher compression ratios, then storage efficiency and transmission bandwidth are improved, but biological information accuracy deteriorates
Solution Approach 1:
A neural network upsampling model is introduced as an intermediary component between the lossy compression decoder and the downstream analysis systems. This model takes the compressed low-resolution genomic data as input and generates high-resolution reconstructed data that preserves biological information, thereby mediating between the compression requirement and the information preservation requirement
Solution Approach 2:
Traditional mechanical or algorithmic upsampling methods are replaced with a neural network-based deep learning system. The neural network learns complex non-linear mappings from compressed to high-resolution data, substituting conventional signal processing approaches with intelligent computational models that better preserve biological patterns
2Manufacturing precision
If traditional upsampling methods are used on decompressed genomic data, then data resolution is improved, but compression artifacts and noise are amplified
Solution Approach 1:
The neural network is trained to recognize and utilize the specific patterns of compression artifacts as part of the learning process. By exposing the network to compressed data during training, it learns to distinguish between genuine biological signals and compression-induced distortions, effectively converting the harmful artifacts into learnable features that improve reconstruction accuracy
Solution Approach 2:
The system changes the fundamental parameters of the upsampling process by using learned non-linear transformations instead of fixed linear interpolation methods. The neural network dynamically adjusts reconstruction parameters based on the input compressed data characteristics, enabling adaptive recovery that preserves signal integrity while eliminating artifacts
3Productivity
If existing machine learning approaches are applied to genomic data analysis, then analytical capabilities are improved, but computational resource requirements increase
Solution Approach 1:
The neural network model is pre-trained on large datasets of genomic sequences and their corresponding high-resolution representations. This preliminary training phase captures general patterns and relationships in genomic data, allowing the model to perform efficient inference on new compressed data without requiring extensive computational resources during actual analysis
Solution Approach 2:
The genomic data processing pipeline is segmented into distinct stages: lossy compression, neural network upsampling, and downstream analysis. Each stage operates independently with optimized computational requirements, allowing the system to balance resource consumption across different processing phases while maintaining overall analytical capability
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.