Genome Feature Extraction Using Base Quality Confidence
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Solution Overview
Problem
Current genome feature extraction methods, such as Deepvariant and Clair, either provide comprehensive but inefficient feature extraction due to high computational requirements or incomplete extraction by ignoring base quality, limiting the accuracy and efficiency of gene sequencing analysis.
Innovation Solution
A method that determines a confidence level based on the base quality of a gene segment and performs feature extraction using this confidence level to integrate quality into gene features without increasing data dimensionality, combining statistical counting with three-dimensional convolutional neural networks for mutation detection and disease prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive feature extraction methods like Deepvariant are used, then feature completeness is improved, but computational efficiency deteriorates due to high computational requirements
Solution Approach 1:
The patent extracts only the essential base quality information from sequencing data to determine confidence levels, rather than processing all comprehensive features. This selective extraction of critical quality metrics maintains feature reliability while significantly reducing computational complexity and improving processing efficiency.
Solution Approach 2:
The patent applies different processing strategies to different parts of the data based on their quality characteristics. High-confidence regions are processed with standard extraction, while low-confidence regions trigger additional verification or alternative processing paths, optimizing computational resources according to local data quality needs.
2Device complexity
If base quality information is ignored in feature extraction, then computational complexity is reduced, but extraction completeness deteriorates
Solution Approach 1:
The patent performs preliminary determination of confidence levels based on base quality information before the main feature extraction process. This preliminary assessment allows the system to prepare appropriate processing strategies in advance, ensuring that quality information is integrated efficiently without adding significant computational complexity to the overall process.
3Measurement precision
If base quality information is integrated into gene features, then feature accuracy is improved, but data dimensionality increases
Solution Approach 1:
The patent transforms base quality information into confidence level parameters that are integrated into the existing feature framework. By changing the representation of quality information from raw base quality scores to normalized confidence levels, the patent improves feature accuracy while maintaining compatibility with existing processing architectures and avoiding significant dimensionality increases.
Data Source
AI summary
The present disclosures provide a genome feature extraction method, a disease prediction method, an apparatus and a device. The feature extraction method includes: obtaining a gene segment to be processed, the gene segment including a base quality; determining a confidence level corresponding to the gene segment based on the base quality; and performing a feature extraction operation on the gene segment based on the confidence level corresponding to the gene segment to obtain gene features of the gene segment. The embodiments effectively achieve an effective integration of the base quality into the gene features without increasing the dimensionality of data. In this way, not only the mode of implementation is simple and reliable and the completeness of extraction of gene features is ensured, but also the operation efficiency of an extraction operation on the gene features is improved.