Addressing Gene Expression Profile Analysis Problems from a Factor Analysis Perspective

This paper addresses gene expression profile analysis challenges through factor analysis principles. To mitigate the instability inherent in traditional Independent Component Analysis during solution convergence, we developed an integrated classifier for DNA microarray data utilizing selective ICA. Our methodological implementation comprises three key computational phases: first, we perform reconstruction error analysis on gene expression levels and algorithmically select independent components demonstrating minimal reconstruction errors for sample reconstruction; second, we concurrently train multiple SVM base classifiers using the reconstructed samples through parallel processing techniques; finally, we implement a majority voting scheme that selectively incorporates base classifiers achieving superior classification accuracy rates. Validation experiments conducted on three widely-used benchmark datasets demonstrate the computational efficacy and robustness of our proposed framework through comparative performance metrics. The core algorithm involves matrix decomposition operations for ICA computation, where we implement error threshold filtering to select optimal components. The SVM training phase employs kernel function optimization with cross-validation for parameter tuning. The voting mechanism incorporates weighted decision fusion based on individual classifier performance scores, ensuring enhanced classification stability compared to conventional single-model approaches.