Gene Expression Analysis Using Z-Score Standardization for Disease Diagnosis
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Solution Overview
Problem
Current methods for determining the presence of diseases based on gene expression levels suffer from high false-positive detection, measurement errors, and poor reproducibility, making it difficult to accurately identify significant changes in gene expression.
Innovation Solution
A method that involves measuring the levels of transcription products of genes in a biological sample using disease-determining gene families, standardizing the data, and applying discriminant analysis to determine the presence of a target disease, utilizing nucleic acid chips and statistical methods like support vector machines for accurate diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive analysis of gene expression levels is performed, then the ability to detect disease-related genes is improved, but the detection of false-positive genes and measurement errors increases
Solution Approach 1:
The patent segments the exhaustive gene expression data into specific gene families (oxidative stress-related, inflammation-related, apoptosis-related, etc.) and selects representative genes from each family. This segmentation approach reduces the complexity of analyzing all genes while maintaining diagnostic accuracy by focusing on biologically relevant gene groups.
Solution Approach 2:
The patent transforms the raw gene expression data into standardized scores (z-scores) by comparing individual gene expression levels against control subject data. This parameter transformation enables statistical evaluation and reduces measurement variability, improving reliability while maintaining detection precision.
2Measurement precision
If statistical techniques are applied to analyze gene expression data, then the ability to distinguish significant changes is improved, but the complexity of the diagnostic system increases
Solution Approach 1:
The patent divides the gene expression analysis into discrete gene family categories (oxidative stress, inflammation, apoptosis, etc.) with selected representative genes in each category. This segmentation simplifies the statistical analysis by reducing the number of genes to be analyzed while maintaining the ability to detect significant disease-related changes through structured comparison.
3Reliability
If conventional gene expression analysis methods are used, then the diagnostic capability is improved, but the false-positive detection rate increases
Solution Approach 1:
The patent applies z-score standardization to transform raw gene expression data into statistically comparable values. By calculating the deviation of each gene's expression level from the control mean in units of control standard deviation, the method enables accurate distinction between biologically significant changes and measurement noise, thereby reducing false-positive detections while maintaining diagnostic reliability.
Solution Approach 2:
The patent performs preliminary standardization of gene expression data against control subject data before diagnostic evaluation. This preliminary action establishes a baseline for comparison and filters out non-specific variations, allowing subsequent analysis to focus on disease-relevant changes with reduced false-positive rates.
Data Source
AI summary
The invention provides a method for determining presence of a disease, comprising steps of; measuring the levels of expression of transcription products of genes in a biological sample obtained from a subject suspected of having a target disease, wherein the genes comprise at least one gene belonging to each of at least two disease-determining gene families related to the target disease; obtaining values representing deviations by standardizing the levels of the expression based on the levels of expression of transcription products of the corresponding genes in a plurality of healthy subjects; obtaining the average of values representing deviations with respect to the gene belonging to each of the disease-determining gene families; and determining whether or not the subject has the target disease by using the average; as well as a computer program product for determining presence of a disease.