Machine Learning Variant Calling for Genetic Carrier Status
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Solution Overview
Problem
Current methods for identifying genetic conditions from sequenced genomes require significant human involvement and are inefficient in determining the number of functional copies of specific genes and the nature of mutations, particularly for autosomal dominant and recessive conditions.
Innovation Solution
The use of machine learning algorithms, specifically recurrent neural networks (RNNs), to analyze copy number data and single nucleotide polymorphism (SNP) data from sequenced genomes to determine the carrier status and functional copy numbers of genes, reducing the need for human intervention by providing accurate and automated diagnoses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning algorithms are used to automate genetic condition identification, then productivity and accuracy are improved, but device complexity increases
Solution Approach 1:
The patent replaces manual human analysis of genetic sequencing data with machine learning algorithms, specifically neural networks. The system automatically processes copy number data and SNP data to determine carrier status and functional gene copies, eliminating the need for human experts to manually interpret complex genetic data patterns.
Solution Approach 2:
The machine learning system is designed to autonomously perform the complete analysis workflow: receiving raw sequencing data, processing copy number and SNP information, applying trained models to determine carrier status, and generating diagnostic conclusions without requiring human intervention at each step. The system self-corrects and self-optimizes through continuous learning from training data.
2Loss of time
If machine learning algorithms are used to reduce human involvement, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The system performs preliminary training of machine learning models using extensive datasets of known genetic conditions before actual diagnostic use. This pre-training phase prepares the algorithms to immediately process new data without requiring human guidance, enabling rapid automated analysis from the start of clinical deployment.
Solution Approach 2:
The patent substitutes human expert time with automated machine learning processing. Neural networks continuously analyze copy number and SNP data in parallel, eliminating sequential human review steps and providing immediate diagnostic results without the time constraints of manual analysis.
3Measurement precision
If automated machine learning determination is used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system transforms raw genetic sequencing data into multiple derived parameters including copy number ratios, SNP allele frequencies, and confidence scores. Machine learning models process these transformed parameters to determine carrier status, using parameter transformations to enhance the discriminative power and precision of genetic variant detection.
Solution Approach 2:
The machine learning system incorporates feedback mechanisms where prediction confidence levels are continuously monitored and adjusted. When uncertainty is detected, the system can request additional sequencing data or flag cases for review, continuously improving precision through iterative learning from outcomes and adjusting model parameters based on performance feedback.
Data Source
AI summary
Methods for determining a respective carrier status of an individual are disclosed. In some examples, the method includes determining the respective carrier status based on copy number data for a gene in a genome of the individual and SNP data for the gene using a machine learning algorithm. In some examples, the machine learning algorithm is configured to receive, as inputs, the copy number data and the SNP data, and output the respective carrier status of the individual.