Deep Learning Prenatal Testing for Copy Number Variation Detection
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Solution Overview
Problem
Existing nucleic acid sequencing methods for detecting copy number variation and genomic abnormalities suffer from low genomic resolution and require labor-intensive processes, lacking standardization across testing laboratories and efficient multiplexed testing capabilities.
Innovation Solution
Application of machine learning techniques, particularly deep learning algorithms, to analyze nucleic acid sequence data for detecting copy number variations and genomic abnormalities, potentially replacing conventional alignment and bias correction steps, and enabling accurate detection of normal, over-representation, or under-representation of genomic sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional alignment and bias correction steps are used, then the detection process is standardized, but the genomic resolution remains low and the process is labor-intensive
Solution Approach 1:
The patent replaces conventional mechanical alignment and bias correction procedures with a machine learning-based system. The deep learning model automatically processes sequencing data to detect copy number variations and genomic abnormalities, eliminating the need for manual alignment steps and reducing labor-intensive operations while significantly improving genomic resolution
Solution Approach 2:
The invention changes the analytical parameters by using machine learning algorithms that can process and interpret sequencing data in novel ways. The system transforms raw sequencing reads into actionable genomic insights through trained models, enabling detection of subtle copy number variations that conventional methods miss, thereby improving measurement precision without proportionally increasing complexity
2Measurement precision
If machine learning techniques are applied, then the accuracy of genomic abnormality detection is enhanced, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large datasets of normal and abnormal genomic sequences before actual diagnostic use. This pre-training phase captures complex patterns and relationships, enabling the model to achieve high detection accuracy during clinical application without requiring complex real-time computations, thus resolving the contradiction between accuracy and computational complexity
Solution Approach 2:
The invention uses copying by creating trained machine learning models that can be replicated and deployed across multiple testing laboratories. Once a model achieves high accuracy, it can be copied and standardized across different systems, maintaining consistent high-level detection accuracy while avoiding the need to redevelop complex algorithms at each facility, thereby managing computational complexity
3Adaptability or versatility
If conventional sequencing methods are used, then the testing process is simple, but multiplexed testing capabilities are limited and sample volume requirements are high
Solution Approach 1:
The patent implements universality by designing a machine learning system that can simultaneously analyze multiple genomic targets and detect various types of abnormalities (copy number variations, aneuploidy, structural variants) from a single sequencing run. This multi-functional approach enables multiplexed testing capabilities, allowing the system to handle diverse testing requirements with reduced sample volume compared to conventional single-target methods
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.