Gene Variant Pathogenicity Classification via Network Analysis
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Solution Overview
Problem
Current methods for determining the pathogenicity of genetic variants are inadequate, as they often classify mutations as 'likely benign,' 'likely pathogenic,' or 'unknown significance' without providing sufficient information on their impact on protein function and disease causation.
Innovation Solution
The development of methods involving the use of expression data, phenotypic and genotypic data, and machine learning algorithms to identify gene networks associated with phenotypic outcomes, allowing for the scoring of pathogenicity and classification of genetic variants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional classification tools are used to categorize genetic variants, then the classification process is simple and quick, but the accuracy and reliability of pathogenicity determination is insufficient
Solution Approach 1:
The patent combines multiple data types (gene expression data, phenotypic data, genotypic data) and integrates them with machine learning algorithms to create a comprehensive pathogenicity assessment system. This merging of diverse data sources and methodologies resolves the contradiction by achieving high reliability through synthesis while managing complexity through structured integration frameworks.
Solution Approach 2:
The system employs multi-functional machine learning models that can process various data types (expression profiles, phenotypes, genotypes) and apply them across different genes and conditions. This universal approach enables accurate pathogenicity determination for diverse genetic variants without requiring separate specialized tools for each case, thereby improving reliability while reducing overall system complexity.
2Measurement precision
If comprehensive gene network analysis is performed to determine pathogenicity, then the accuracy of pathogenic classification is improved, but the computational time and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing gene expression data, phenotypic data, and genotypic data into structured formats before actual pathogenicity analysis. This includes normalizing expression data, annotating gene networks, and preparing training datasets in advance. By completing these preparatory steps beforehand, the system reduces computational time during actual analysis while maintaining comprehensive network analysis for high precision pathogenicity determination.
Solution Approach 2:
The system dynamically adjusts analysis parameters based on the specific gene being evaluated, the available data quality, and computational resources. Machine learning models can modify their processing depth and data requirements based on input characteristics, allowing the system to maintain high measurement precision while optimizing processing time by reducing computational intensity for cases where comprehensive analysis is not necessary.
Data Source
AI summary
Provided are methods of determining the pathogenicity of a genetic variant, the method comprising: selecting a gene of interest; identifying a network of genes or gene variants that are up-regulated, down-regulated, or co-expressed with the gene of interest, and/or that interact directly or indirectly with the gene of interest; and determining the pathogenicity of the gene of interest based on the presence, absence, and/or expression levels of the network of genes. Also provided are methods of constructing a pathogenicity classifier for a genetic variant, the method comprising training a pathogenicity classifier.