Multilayer PCA Classifier for Gene Expression Analysis
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Solution Overview
Problem
Current classification algorithms face challenges in accurately diagnosing diseases due to high dimensionality of gene expression data, leading to data overfitting and computational burdens, especially when sample sizes are small, limiting their ability to identify key genetic variations and provide timely and effective treatments.
Innovation Solution
A multilayer Principal Component Analysis (PCA) classification method that extracts a subset of fingerprint genes from gene expression profiles, reducing dimensionality while maintaining information, to classify patients as diseased or healthy, and identify specific disease features, thereby aiding in early disease detection and personalized medicine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classification algorithms use all gene expression data, then comprehensive disease detection is achieved, but data overfitting occurs and computational burden increases
Solution Approach 1:
The patent extracts a subset of most relevant genes from the complete gene expression dataset using feature selection techniques. This extraction process removes redundant and irrelevant genes, retaining only the most informative features for classification, thereby reducing overfitting and computational complexity while maintaining diagnostic accuracy
Solution Approach 2:
The patent segments the high-dimensional gene expression data into manageable subsets through hierarchical clustering and feature selection. By dividing the comprehensive gene set into smaller, more relevant groups, the system reduces computational burden and prevents overfitting while preserving the essential diagnostic information
2Reliability
If classification algorithms use all gene expression data, then comprehensive disease detection is achieved, but computational burden increases
Solution Approach 1:
The patent extracts a subset of most relevant genes from the complete gene expression dataset using feature selection techniques. This extraction process removes redundant and irrelevant genes, retaining only the most informative features for classification, thereby reducing overfitting and computational complexity while maintaining diagnostic accuracy
Solution Approach 2:
The patent applies partial action by using only the necessary subset of genes rather than processing the entire gene expression dataset. This selective approach processes fewer features (most relevant genes) while still achieving accurate disease detection, significantly reducing computation time and resources
3Reliability
If small sample sizes are used, then patient privacy is protected, but classification accuracy decreases due to high dimensionality
Solution Approach 1:
The patent extracts a subset of most relevant genes from the complete gene expression dataset using feature selection techniques. This extraction process removes redundant and irrelevant genes, retaining only the most informative features for classification, thereby reducing overfitting and computational complexity while maintaining diagnostic accuracy
Solution Approach 2:
The patent changes the parameter of feature dimensionality by selecting a reduced set of most relevant genes. This parameter transformation converts the high-dimensional gene expression space into a lower-dimensional space defined by the most informative genes, enabling accurate classification even with limited sample sizes
Data Source
AI summary
A diagnostic process for disease detection using gene expression based PCA (Principal Component Analysis) classifier is provided. The present invention includes a method of diagnosing disease in a patient by performing a multilayer PCA classification that analyzes gene expression profiles of patients' biological samples to predict their class as disease or healthy, uses patients' biological samples to extract a set of fingerprint genes for specific disease and cell type which can be used as identification features, and classifies patient biological samples as disease or healthy based on differential gene expression profiles of the fingerprint genes. The present invention also implements the multilayer PCA classifier on patients' biological samples to extract a set of fingerprint genes for specific disease and cell type, and tabulates the fingerprint gene information in a database that will be referenced for disease diagnosis, prescreening in early stages of disease, and for confirming the stage of the disease, executed through the many embodiments of the invention.