Classical Approaches in Gene Evaluation for Carcinogenic Disease Detection

This chapter explores the critical role that microarray technology plays in computational biology, providing a powerful tool for detecting anomalies in the human body through gene expression information. Microarray data sets are critical to the automated detection of carcinogenic diseases, as they facilitate manual pathological diagnosis methods. The challenges arise from the vast number of genes within limited accessible samples, making the analysis of gene expression levels intricate. Two classical gene selection strategies, namely filter and wrapper methodologies, are explored in this chapter. In filter techniques, genes are ranked statistically, while in wrapper approaches, genes are repeatedly chosen depending on classification outcomes. Although they are predicted to perform better than filter methods, wrapper approaches have a higher computational cost. With a focus on SRBCT, ALL_AML, and MLL subclasses of cancer, the chapter seeks to distinguish cancer types based on patterns of gene expression. For a diagnosis to be both accurate and efficient, filter- and wrapper-based gene selection techniques have been examined. The chapter delves more into gene ranking techniques based on filter methods with binary and multi-class data sets. Additionally, it elaborates on the utility of Particle Swarm Optimization (PSO) methodology in wrapper-based gene selection from microarray gene expression data, contributing to the understanding of effective diagnostic methodologies.