Diagnostic Method Using Pseudo-Concentrations for Lower Abundance Biomarkers
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Solution Overview
Problem
Current disease diagnosis methods using multiple analytes struggle with predictive accuracy due to reliance on high abundance proteins and DNA markers, which often result in high false positives and negatives, and fail to effectively utilize lower abundance proteins and meta-variables associated with immune system responses.
Innovation Solution
The method involves determining concentrations of analytes in a biological sample, applying meta-variables to adjust these concentrations, and using correlation methods like clustering, regression, or wavelet analysis to compute pseudo-concentrations for improved diagnostic accuracy, incorporating population distribution characteristics and meta-variables such as age, menopausal status, and geographic location to enhance predictive power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If high abundance proteins and DNA markers are used for disease diagnosis, then the diagnostic method is easier to implement, but the predictive accuracy deteriorates with high false positives and negatives
Solution Approach 1:
The patent transforms analyte concentrations into pseudo-concentrations by applying meta-variables and population distribution characteristics. This parameter transformation allows lower abundance proteins to contribute meaningfully to the diagnostic model, improving predictive accuracy while maintaining ease of implementation through standardized computational procedures.
Solution Approach 2:
The patent combines multiple analytes including lower abundance proteins with meta-variables (age, sex, population characteristics) to create a composite diagnostic model. This composite approach leverages the collective information from diverse sources to achieve high predictive accuracy without relying solely on high abundance proteins.
2Reliability
If multiple analytes are used for correlation analysis, then the predictive power improves, but the complexity of the diagnostic method increases
Solution Approach 1:
The patent replaces complex manual analytical procedures with automated computational methods. The transformation of analyte concentrations into pseudo-concentrations using meta-variables and population distribution characteristics is performed through standardized algorithms, reducing method complexity while maintaining high predictive power.
Solution Approach 2:
By transforming raw analyte concentrations into pseudo-concentrations through mathematical operations involving meta-variables, the patent simplifies the interpretation of multiple analytes. This parameter transformation consolidates the information from multiple sources into a unified metric that can be analyzed using standard correlation methods.
3Reliability
If lower abundance proteins are utilized, then the diagnostic accuracy improves, but the difficulty of detecting and measuring these analytes increases
Solution Approach 1:
The patent introduces meta-variables and population distribution characteristics as intermediaries that amplify the diagnostic signal from lower abundance proteins. By transforming these analyte concentrations into pseudo-concentrations, the method makes lower abundance proteins measurable and meaningful in the context of population-specific disease risk.
Solution Approach 2:
The patent applies parameter transformation to convert difficult-to-measure lower abundance protein concentrations into pseudo-concentrations that are more readily analyzed. This mathematical transformation adjusts for population distribution characteristics, making the measurement and interpretation of lower abundance proteins more straightforward.
Data Source
AI summary
Methods for improving clinical diagnostic tests are provided, along with associated diagnostic techniques.