Proximity Score Biomarker Analysis for Disease Diagnosis
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Solution Overview
Problem
Conventional proteomic methods face challenges in accurately diagnosing diseases like cancer and Alzheimer's due to protein concentration measurements being contaminated by factors such as drugs, geographic, and environmental influences, leading to complex non-linear behaviors that are difficult to model effectively.
Innovation Solution
The use of a 'Proximity Score' calculated from concentration values, normalized for age and other physiological parameters, to suppress noise and variances, combined with orthogonal spatial proximity correlation methods involving biomarkers like cytokines, immune system inflammatory markers, and tumor vascularization markers, to improve predictive power and separate disease states from non-disease states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional proteomic methods are used to measure protein concentrations, then diagnostic information can be obtained, but the measurements are contaminated by drugs, geographic, and environmental factors leading to reduced accuracy
Solution Approach 1:
The patent extracts and removes the harmful variance components from the protein concentration measurements through statistical decomposition. By separating the signal (disease-related protein levels) from noise (drugs, geographic, and environmental factors), the method isolates the diagnostically relevant information while eliminating contaminating influences.
Solution Approach 2:
The patent introduces an intermediary statistical model that acts as a mediator between raw protein measurements and diagnostic conclusions. This model decomposes measurements into distinct variance components, allowing the diagnostic process to rely on purified signal rather than contaminated raw data.
2Device complexity
If protein concentration measurements are used directly for disease prediction, then the analysis is simple, but the complex non-linear behaviors of biological systems cannot be effectively modeled
Solution Approach 1:
The patent transforms the analysis by changing parameters from raw protein concentrations to variance components derived through statistical decomposition. This parameter transformation converts complex non-linear biological behaviors into separable variance components that can be systematically analyzed and reliably interpreted for disease prediction.
3Reliability
If normalized Proximity Scores are calculated and orthogonal spatial proximity correlation methods are used, then predictive power increases to 96%, but the computational complexity and method sophistication increase
Solution Approach 1:
The patent segments the diagnostic process into distinct computational stages: variance decomposition, Proximity Score calculation, and orthogonal spatial proximity correlation. This segmentation transforms a seemingly complex monolithic process into manageable sequential steps, each with a specific function, thereby reducing overall computational complexity while maintaining high predictive power.
Data Source
AI summary
The present invention relates to systems and methods for improving the accuracy of disease diagnosis and to associated diagnostic tests involving the correlation of measured analytes with binary outcomes (e.g., not-disease or disease), as well as higher-order outcomes (e.g., one of several phases of a disease). Methods of the present invention use biomarker sets, preferably those with orthogonal functionality, to obtain concentration and proximity score values for disease and non-disease states. The biomarker set's proximity scores are graphed on an orthogonal grid, with one dimension for each biomarker. The proximity scores and orthogonal gridding is then used to calculate a disease state or non-disease state diagnosis for the patient.