Deep Learning Prenatal Testing for High-Precision CNV Detection
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Solution Overview
Problem
Existing nucleic acid sequencing methods for detecting copy number variation and genomic abnormalities are limited by low genomic resolution and require labor-intensive processes, lacking standardization across testing laboratories and efficient multiplexed testing capabilities.
Innovation Solution
Applying machine learning techniques, particularly deep learning algorithms, to analyze nucleic acid sequence data for detecting copy number variations and genomic abnormalities, bypassing traditional alignment steps and utilizing neural network architectures for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alignment-based sequencing analysis methods are used, then the process is well-established and relatively simple to implement, but the genomic resolution is low and the detection precision is limited
Solution Approach 1:
The patent replaces traditional mechanical alignment-based analysis with a machine learning system that processes sequencing reads directly through neural networks. The system transforms raw sequencing data into k-mer frequencies and feeds these directly into trained models, eliminating the need for complex alignment steps while achieving superior genomic resolution and detection precision.
Solution Approach 2:
The patent fundamentally changes the analysis parameters by transitioning from alignment-based positional mapping to k-mer frequency analysis. By converting sequencing reads into frequency distributions of k-mers and using these as direct inputs to machine learning models, the system achieves higher precision in detecting copy number variations and genomic abnormalities.
2Productivity
If conventional sequencing analysis pipelines are used, then the workflow is standardized, but the process is labor-intensive and lacks efficient multiplexed testing capabilities
Solution Approach 1:
The patent merges multiple analysis functions into a single integrated machine learning pipeline. The system simultaneously performs quality assessment, copy number variation detection, and genomic abnormality identification through unified neural network models, eliminating the need for separate labor-intensive analysis steps and enabling efficient multiplexed testing.
Solution Approach 2:
The machine learning models are trained on comprehensive datasets and deployed to autonomously analyze sequencing data without requiring manual intervention or complex operational procedures. The system self-adjusts and provides standardized results across different testing scenarios, significantly improving productivity while maintaining ease of operation.
3Measurement precision
If traditional cytogenetic techniques are used, then the methods are well-established and easy to perform, but the genomic resolution is limited to kilobase to megabase scale
Solution Approach 1:
The patent replaces traditional cytogenetic mechanical observation methods with computational machine learning analysis. By using neural networks to interpret k-mer frequency patterns from sequencing data, the system achieves detection accuracy at the base-pair level, vastly superior to the kilobase-megabase resolution of conventional cytogenetic techniques.
Data Source
AI summary
Methods for applying machine learning algorithms to nucleic acid sequencing-based diagnostics tests for detection of copy number variation and other genomic abnormalities are described.