NPM1-Associated Gene Network Analytics for Pathway-Based Treatment
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Solution Overview
Problem
Current diagnostic and therapeutic interventions often overlook the complex network of molecular interactions within human cells, leading to drug resistance and disease relapse in diseases like cancer, as they analyze disease biomarkers in isolation.
Innovation Solution
A platform utilizing artificial intelligence to analyze dysregulated biological pathways by comparing genome-wide gene expression and co-expression changes, employing a two-layer ensemble machine learning model to identify state of interest risks and molecular targets for personalized drug treatment regimens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If disease biomarkers are analyzed in isolation, then the diagnostic process is simple and fast, but the treatment effectiveness decreases due to drug resistance and disease relapse
Solution Approach 1:
The patent segments the analysis into multiple layers: first layer analyzes individual biomarkers, second layer analyzes gene-gene interactions, and third layer integrates both levels. This segmentation allows the system to capture complex molecular interaction networks while maintaining manageable computational complexity through hierarchical processing.
Solution Approach 2:
The patent transitions from one-dimensional single biomarker analysis to multi-dimensional network analysis by incorporating gene-gene interaction data. This dimensional expansion enables the system to model complex biological systems more accurately, capturing the networked nature of molecular interactions that influence disease progression and treatment response.
2Measurement precision
If complex molecular interaction networks are analyzed, then treatment precision improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and normalizing gene expression data before interaction analysis. It also pre-identifies significant biomarkers through univariate analysis, which reduces the dimensionality of the data and simplifies subsequent network analysis, thereby reducing computational complexity while maintaining detection precision.
Solution Approach 2:
The patent introduces statistical methods and computational algorithms as intermediaries between raw gene expression data and clinical interpretation. These intermediaries process and transform complex interaction data into meaningful insights about disease-associated molecular networks, bridging the gap between high-dimensional data and actionable medical knowledge.
3Quantity of substance
If genome-wide gene expression data is processed, then the identification of molecular targets becomes more comprehensive, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing analysis on the most significant biomarkers and interactions rather than processing all possible gene combinations equally. It uses statistical thresholds and significance criteria to prioritize relevant targets, thereby identifying comprehensive molecular targets while reducing processing time by excluding irrelevant data.
Solution Approach 2:
The patent changes parameters such as significance thresholds, interaction strength cutoffs, and filtering criteria to optimize the balance between comprehensiveness and processing efficiency. By adjusting these parameters, the system can identify a comprehensive set of molecular targets while controlling computational resource consumption and processing time.
Data Source
AI summary
The invention provides a method and system for analyzing dysregulated biological pathways associated with states of interest to identify risks and molecular targets for personalized treatment. The method employs a two-layer machine learning model (MLM) to assign dysregulated pathway (DP) scores to biological pathways derived from both whole-genome co-expression network analysis and differential gene expression analysis. In the first layer, classifiers are used to predict states based on the identified pathways. In the second layer, a stacking classifier integrates these predictions to compute the final state. Each pathway is weighted according to its contribution to the state of interest, and pathway scores are normalized to reflect their relative significance. The method incorporates the Shapley Additive Explanations (SHAP) technique to enhance model interpretability. This enables the identification of key genes and molecular targets. This method and system are patient-independent, offering a framework for precision medicine across a wide range of conditions.