Revolutionizing Oncology: Cutting-edge Classification Methods for Microarray Data

Abstract

Biologists grapple with the complexity of gene expression data, marked by a multitude of genes and limited samples. In the realm of Bioinformatics, pivotal challenges include gene subset selection, cancer profiling, and functional gene elucidation. Researchers are leveraging vast microarray gene expression datasets and employing machine learning on them for early cancer detection, prognosis, and biomarker identification, with a particular focus on survival analysis. The extensive gene dimensions inherent in microarray expression data, coupled with a limited number of patient samples, have ushered in a transformative era in cancer prediction and identification. Leveraging this technological advancement, precise cancer classification hinges on the meticulous selection of genes uniquely associated with each specific cancer subtype, marking a significant stride in the field of oncology research. This analysis delves into recent advancements in utilizing microarray gene expression data for disease diagnosis, particularly in cancer detection, through comprehensive coverage of data preprocessing, dimensionality reduction and machine learning algorithms, including supervised, unsupervised and semi-supervised approaches.