Biomarker Identification via Mechanistic Model Sensitivity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying biomarkers that indicate treatment response in patients are limited by insufficient experimental data and computationally expensive analysis, making it difficult to accurately predict the effectiveness of treatments for complex biological systems.
Innovation Solution
A computer-implemented method that iteratively swaps subsets of individual parameters between responders and non-responders to predict target characteristics, determining deviations and identifying biomarkers based on statistically significant changes, using a mechanistic model to differentiate between treatment responders and non-responders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If permutation analysis methods are used to identify biomarkers, then biomarker identification can be performed, but the analysis becomes computationally expensive and inaccurate with limited experimental data
Solution Approach 1:
The patent transforms the computational approach from permutation-based statistical analysis to sensitivity analysis of mechanistic model parameters. By changing the analytical parameters from data-driven permutation scores to model-based parameter sensitivity measures, the method achieves accurate biomarker identification with reduced computational complexity, directly resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent replaces the mechanical/computational permutation analysis system with a mechanistic modeling system. Instead of repeatedly shuffling and reanalyzing data through computational permutations, the method uses a mechanistic model with sensitivity analysis to identify biomarkers, substituting a computationally intensive data-driven approach with a more efficient model-driven approach while maintaining or improving accuracy
2Reliability
If permutation analysis is applied to limited experimental data, then biomarker screening can be performed, but the results become inaccurate and misleading
Solution Approach 1:
The patent incorporates prior biological knowledge and mechanistic understanding into the modeling framework before analyzing experimental data. By pre-defining mechanistic relationships and parameter meanings based on existing scientific knowledge, the method enables reliable biomarker identification even with limited experimental data, as the mechanistic constraints guide the analysis and reduce dependence on large data volumes
Solution Approach 2:
The patent introduces a mechanistic model as an intermediary between the limited experimental data and the biomarker identification process. This mechanistic model acts as a mediator that translates sparse experimental observations into reliable biomarker predictions by incorporating biological plausibility and mechanistic relationships, thereby improving reliability without requiring large quantities of experimental data
Data Source
Figure 1
AI summary
The present invention relates to a method of identifying markers of a specimen's state with a mathematical model, provided with individual parameters of the specimen and experimentally measured target characteristic of the specimen. A specimen is assumed to be either in state A or in state B depending on the value of its experimentally measured target characteristic. The used mathematical model allows prediction of the target characteristic of a specimen based on predefined parameters corresponding to this specimen.