Gene Expression Dataset Classification Using Machine Learning Methods: A Survey

The process of converting the instructions in the DNA of human beings into a functional product such as proteins is called gene expression. Gene expression is an organized process that allows the cell to adapt to its changing environment after converting the information stored in DNA into instructions, forming the basis for making protein and other molecules. It is a tightly regulated process that allows a cell to respond to its changing environment. Gene expression classification is important because it helps a lot in the prediction, prevention, and early control of some genetic diseases. Bioinformatics is a broad field that specializes in developing software methods and techniques for analyzing and understanding biological data, especially large ones that are difficult to analyze without software assistance. Bioinformatics is not limited to biology and computer sciences only, but chemistry, physics, mathematics, statistics, and information engineering participate in this discipline to help analyze and interpret large-scale biological data. Machine learning methods are used extensively in bioinformatics on the biological data sets and provide good results, such as Random Forest and Support Vector Machine. In this paper, a comprehensive survey is made of the recently used machine learning methods for gene expression classification.