Gene Expression Analysis for Lupus Disease State Classification
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Solution Overview
Problem
Current methods struggle to accurately classify the disease state of patients with systemic lupus erythematosus (SLE) due to the heterogeneous nature of the disease, which affects treatment efficacy.
Innovation Solution
A method involving the analysis of gene expression data to select a gene set capable of classifying disease states by clustering variably expressed genes into significant gene clusters based on co-expression and correlation with sample traits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current classification methods are used for SLE disease states, then the classification process is simple, but the classification accuracy is low due to disease heterogeneity
Solution Approach 1:
The patent segments the heterogeneous SLE disease population into distinct endotypes based on gene expression profiles. By clustering patients into specific subgroups (endotypes) with characteristic gene expression patterns, the method achieves more accurate classification of disease states while accounting for the heterogeneous nature of SLE.
Solution Approach 2:
The patent transforms the classification approach by changing from clinical parameters to gene expression parameters. By using transcriptomic data and identifying specific gene signatures associated with different endotypes, the method improves classification accuracy by capturing molecular mechanisms underlying disease variation.
2Measurement precision
If gene expression analysis is performed to classify disease states, then classification precision is improved, but the amount of data processing and analysis required increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing gene expression data and pre-identifying endotype-specific gene signatures from reference datasets. This preliminary analysis creates a framework that can be applied to new patient samples more efficiently, reducing the time required for subsequent classifications.
Solution Approach 2:
The patent creates a reference model or copy of the disease classification framework by analyzing reference samples and establishing endotype definitions. This reference framework can then be applied to new patient data, reducing the computational burden compared to performing complete analysis from scratch for each patient.
Data Source
AI summary
The present disclosure provides systems and methods for classifying lupus disease state of a patient is disclosed. The method can include analyzing a patient data set comprising or derived from gene expression measurements data of at least 2 genes, from a biological sample obtained or derived from the patient, to classify the lupus disease state of the patient. The at least 2 genes can be selected from Tables 17-1 to 17-30, and/or Tables 24-1 to 24-30.