Gene Expression Data Analysis Using Machine Learning And Deep Learning Techniques For Cancer Microarray Data

Abstract

Microarray technology has emerged as a pivotal tool in cancer research, providing a highthroughput newlineplatform for simultaneously analyzing gene expression profiles across thousands newlineof genes. This technology has revolutionized cancer detection and classification by enabling newlineresearchers to uncover critical biomarkers, identify molecular subtypes of cancer, and predict newlineclinical outcomes with remarkable precision. Despite its immense potential, the effective newlineutilization of microarray data in cancer classification poses significant challenges due to its newlineunique characteristics and inherent complexities. Microarray datasets are typically newlinecharacterized by a high dimensionality of features and thousands of gene expressions newlinecombined with a limited number of samples. This phenomenon, often called the quotlarge p, newlinesmall nquot problem, severely impacts the performance of machine learning models, leading to newlineoverfitting and poor generalization of unseen data. Additionally, noise and variability arising newlinefrom technical inconsistencies in sample preparation and processing further complicate the newlinedevelopment of reliable and robust cancer detection models. newlineThis thesis addresses these challenges through innovative methodologies to enhance the newlineaccuracy, reliability, and interpretability of cancer detection using microarray data. We newlinepropose an advanced feature selection framework incorporating the Improved Binary Grey newlineWolf Optimizer (IBGWO) to tackle the high dimensionality issue. This optimization newlinealgorithm effectively selects an optimal subset of features, significantly reducing the newlinedimensionality while retaining the most relevant and informative gene expressions. The newlinereduced feature set mitigates overfitting and ensures the predictive models achieve superior newlinerobustness and accuracy. Furthermore, we introduce hybrid feature selection methods that newlinesynergize filter and wrapper techniques. In this two-tiered approach, filter methods serve as an newlineinitial screening mechanism to eliminate irrelevant features, while wrapper methods, such as newlineMoth-flam