Integrating Machine Learning in Microarray Analysis: New Frontiers in Feature Selection

Abstract

Machine learning plays a pivotal role in various fields today, including life sciences (like bioinformatics and biotechnology) and medical research. It aids in managing and analyzing the massive amount of biomedical information we gather. Machine learning brings numerous benefits, such as uncovering patterns in clinical research, enhancing access to treatments for various diseases at reduced costs, identifying disease causes, and determining effective medical treatments. Recent developments in machine learning have resulted in efficient methods for extracting significant patterns from large, high-dimensional databases. Merging biotechnology with information technology is crucial for proficiently mining biological data. The analysis of gene expression profiles, essential in biological research, is significantly improved by machine learning. This technology is particularly potent in analyzing hundreds or thousands of genes simultaneously in a single experiment, such as in DNA microarray technology. The paper discusses current machine learning techniques that support bioinformatics in handling large datasets like Microarray datasets. It also reviews various feature selection methods for high-dimensional data, current approaches, and outlines research challenges that may drive further progress in the Knowledge Discovery in Databases (KDD) process. Various machine learning approaches, employing different tools and techniques, are applied to analyze diverse types of gene expression data using feature selection algorithms.